def determineResidualUtility(compromisedComponents,rD,debug=True):
rMax = addRisksToLogic(rD)
maxUtility = sumAllUtilities()
print(("Maximum Utility with No Vulnerabilities: " + str(sumAllUtilities())))
#query = "worstCasePathFromSource[SourceService,TotalC]"
#utilitiesByAttackerCapability = pyDatalog.ask(query).answers
combos = compromisedCombos(compromisedComponents)
expectedValue = 0
for combo,p in combos:
print("Evaluating compromise combination:" + str(combo))
for ccs in combo: #ccs is the component stripped out of the list
#print "ccs:" + str(ccs)
#Add in compromised components to logic
#+ componentCompromisedWithAttributes('vpn',0.1,False,False,False)
pyDatalog.assert_fact("componentCompromisedWithAttributes",str(ccs),str(p),"False","False","False")
if debug:
print("Probability for this combination: " + str(p))
#query = "cToWithPrivileges(IntermediateService1,TargetService,VulnType,C)"
#print("Connections in Graph:")
#pprint.pprint(pyDatalog.ask(query).answers)
#print("Probability for this combination: " + str(p))
#query = "consumesPath(FunctionA,TargetService,Data,P)"
#print("Consumption Paths in Graph:")
#pprint.pprint(pyDatalog.ask(query).answers)
expectedValue += determineResidualUtilityHelper(rD,maxUtility) * p
for ccs in combo:
#Remove compromised components from logic
pyDatalog.retract_fact("componentCompromisedWithAttributes",str(ccs),str(p),"False","False","False")
print("Final Expected Value:" + str(expectedValue))
return expectedValue
def retractAll(pname, arity):
"""
retract all facts of given predicate name and arity from the LogKB.
"""
vnames = ["V" + str(x) for x in range(0,arity)]
varlist = "(" + ",".join(vnames) + ")"
query = pname + varlist
ans = pyDatalog.ask(query)
for r in ans.answers:
pyDatalog.retract_fact(pname,*list(r))
def retractAll(pname, arity):
"""
retract all facts of given predicate name and arity from the LogKB.
"""
vnames = ["V" + str(x) for x in range(0, arity)]
varlist = "(" + ",".join(vnames) + ")"
query = pname + varlist
ans = pyDatalog.ask(query)
for r in ans.answers:
pyDatalog.retract_fact(pname, *list(r))
def retractPatient(self, name):
Logic(self.first)
#Método para olvidar el paciente, recupera sus datos de la base de conocimiento y lo elimina
returnvalue = ''
try:
tq1 = "age(" + name + ",Y)"
tq2 = "sick_of(" + name + ",Y)"
q1 = pyDatalog.ask(tq1)
q2 = pyDatalog.ask(tq2)
pyDatalog.retract_fact('age', name, q1.answers[0][0])
pyDatalog.retract_fact('sick_of', name, q2.answers[0][0])
return 'retracted'
except:
returnvalue = "NOPATIENT"
return returnvalue
def eliminar(nombre):
W = pyDatalog.ask("is_person('" + nombre + "')")
if (str(W) == 'None'):
print("Este nombre no lo conozco")
else:
pyDatalog.retract_fact('is_sport_person', nombre)
pyDatalog.retract_fact('is_person', nombre)
pyDatalog.retract_fact('is_exited', nombre, '3', '600')
pyDatalog.retract_fact('has_interest_in_sports', nombre, '4', '800')
def retractpreferences(self):
Logic(self.first)
returnvalue = ''
try:
db = self.con.conexion()
coleccion = db['sensor_temp']
query = {"activo": "t"}
req = list(coleccion.find(query))[0]
tq1 = "prefer_temperature(" + req['enfermedad'] + "," + req[
'stage'] + ",X)"
q1 = pyDatalog.ask(tq1)
pyDatalog.retract_fact('prefer_temperature', req['enfermedad'],
req['stage'], q1.answers[0], [0])
pyDatalog.assert_fact('prefer_temperature', req['enfermedad'],
req['stage'], req['temp_pref'])
return 'retracted'
except:
returnvalue = "NOPATIENT"
return returnvalue
def main():
r = RedisManager(host=RedisConfig['host'], port=RedisConfig['port'], db=RedisConfig['db'],
password=RedisConfig['password'], decodedResponses=RedisConfig['decodedResponses'])
sub = r.getRedisPubSub()
sub.subscribe(RedisConfig['VocalChannel'])
learn()
while True:
newMsg = sub.get_message()
if newMsg:
if newMsg['type'] == 'message':
now = getTimestamp()
msgContent = newMsg['data'].decode()
print("Vocal Result: " + str(newMsg)) # Test
facialRes = getAverageEmotionsFromRedisQueue(r, queue=RedisConfig['FacialQueue'],
emotions=DMAConfig['emotions'])
if msgContent == str(RedisConfig['voidMsg']):
facialVocal = facialRes
else:
vocalRes = ast.literal_eval(msgContent)
facialVocal = facialVocalCompare(facialRes, vocalRes, emotions=DMAConfig['emotions'],
facialW=DMAConfig['facialWeight'], vocalW=DMAConfig['vocalWeight'])
print("FACIAL VOCAL:" + str(facialVocal)) # Test
if facialVocal:
sortedEmotions = {k: v for k, v in sorted(facialVocal.items(), key=lambda item: item[1])}
print(sortedEmotions) # Test
sortedEmoList = list(sortedEmotions.items())
firstEmotion = sortedEmoList[-1]
secondEmotion = sortedEmoList[-2]
topEmotions = dict([firstEmotion, secondEmotion])
decisionWithPer = firstEmotion
diff = 0
for k in topEmotions:
diff -= topEmotions[k]
diff = abs(diff)
attitude = getAverageAttitudeFromRedisQueue(r, queue=RedisConfig['PoseQueue'])
print("DATA: " + str(firstEmotion) + ", " + str(secondEmotion) + ", " + str(attitude))
if diff <= DMAConfig['poseTestThreshold']:
if attitude:
pyDatalog.assert_fact('firstEmoPercept', firstEmotion[0])
pyDatalog.assert_fact('secondEmoPercept', secondEmotion[0])
pyDatalog.assert_fact('poseAttitudePercept', attitude)
decision = str(pyDatalog.ask("takeDecision(D)")).replace("{('", "").replace("'", "").split(",")[0]
decisionWithPer = (decision, str(topEmotions[decision]))
pyDatalog.retract_fact('firstEmoPercept', firstEmotion[0])
pyDatalog.retract_fact('secondEmoPercept', secondEmotion[0])
pyDatalog.retract_fact('poseAttitudePercept', attitude)
r.setOnRedis(key=RedisConfig['DecisionSet'], value=str(decisionWithPer))
r.publishOnRedis(channel=RedisConfig['newDecisionPubSubChannel'], msg=str(decisionWithPer))
print("Decision: " + str(decisionWithPer)) # Test
r.deleteRedisElemsByKeyPatternAndTimestamp(RedisConfig['imageHsetRoot'] + '*', now,
DMAConfig['timeThreshold'])
r.deleteRedisElemsByKeyPatternAndTimestamp(RedisConfig['audioHsetRoot'] + '*', now,
DMAConfig['timeThreshold'])
def determineResidualUtility(debug=False):
rMax = riskDistribution.maxRisk()
#Uncomment this after debugging
#Time hog?
#if debug:
print "Calculating attack scenarios..."
costsPlus = pyDatalog.ask("allAttackerPathsCostPlus(SourceService,TargetService,P,E,F,U,TotalC," + str(rMax) + ")")
#if debug:
print "Scenarios calculated."
#if debug:
# print "Query: " + "allAttackerPathsCostPlus(SourceService,TargetService,P,E,F,U,TotalC," + str(rMax) + ")"
#costsPlus = pyDatalog.ask("allAttackerPathsCostPlus(SourceService,TargetService,P,E,F,U,TotalC," + str(1) + ")")
if costsPlus != None:
costsPlusSorted = sorted(costsPlus.answers, key=itemgetter(5))
if debug:
print "No attack traces"
else:
costsPlusSorted = []
if debug:
print "Attack traces:"
pprint.pprint(costsPlusSorted)
#qFs = pyDatalog.ask("functionQuestionable(FuncName,Util)")
#print("Questionable functions:")
#print(qFs.answers)
compromisedComponents = pyDatalog.ask("probCompromised(SourceService,Prob)")
if compromisedComponents != None:
#if debug:
# print "Compromised Components x:"
# print compromisedComponents.answers
combos = getCombinations(compromisedComponents.answers,debug)
else:
#if debug:
print "Nothing compromised"
sumOverCombos = 0
compromisedAllPossibleAnswer = pyDatalog.ask("compromised(SourceService)").answers
compromisedAllPossible = [] #List of all possible compromised components
if compromisedAllPossibleAnswer != None:
for c in compromisedAllPossibleAnswer:
compromisedAllPossible.append(c)
#Loop over each possible combination of compromised components
#print "Combos: " + str(combos)
for compromiseCombo in combos:
compromisedComponents = compromiseCombo[0]
prob = compromiseCombo[1]
if debug:
print "Compromised Components:"
print compromisedComponents
print "Prob:"
print prob
#New Code to remove from database things that aren't compromised this iteration
#There are probably better ways to do this for performance
for cc in compromisedAllPossible:
if cc[0] not in compromisedComponents:
if debug:
print "Remove " + str(cc[0])
pyDatalog.retract_fact("compromised",str(cc[0]))
#Was this right to comment out?
#costsPlus = pyDatalog.ask("allAttackerPathsCostPlus(SourceService,TargetService,P,E,F,U,TotalC," + str(rMax) + ")")
#End New Code
costsPlusSortedLimited = costsPlusSorted #These are just the attack traces with the currently compromised components
#print costsPlusSorted
#possibleConnections = itertools.ifilter(lambda connectionPair: connectionDoesNotExist(connectionPair),allPossibleConnections)
#print CostPlusSorted[0][0]
costsPlusSortedLimited = [trace for trace in costsPlusSorted if trace[0] in compromisedComponents]
if debug:
print "CostPlusSorted Length: "
print len(costsPlusSorted)
print len(costsPlusSortedLimited)
#Find the worst case scenario for any given level of attacker capability
riskUtilDict = {}
if debug:
fDict = {}
#print "BROKEN CONNECTIONS:"
#pprint.pprint(pyDatalog.ask("transitiveConnectionBroken(SourceService,TargetService)").answers)
#print "RTU CONNECTIONS"
#pprint.pprint(pyDatalog.ask("transitiveConnects('rtus',TargetService)").answers)
downOrCompUtil = 0
if False:
downFunctions = pyDatalog.ask("functionDown(FunctionA,U)") # Required connections are down
compromisedFunctions = pyDatalog.ask("functionCompromised(FunctionA,U)")
if downFunctions != None:
downUtils = downFunctions.answers
#if debug:
print "Down Utilities:"
print downUtils
#for [f,u] in downUtils:
#print u
#downUtil += u
if compromisedFunctions != None:
compromisedFs = compromisedFunctions.answers
#if debug:
print "Compromised Utilities:"
print compromisedFs
#Time hog?
#if debug:
print "Calculating Down and Compromised Functions"
downOrCompromisedFunctions = pyDatalog.ask("functionDownOrCompromised(FunctionA,U)")
#if debug:
print "Down and Compromised Functions Calculated"
if downOrCompromisedFunctions == None:
if debug:
print "Nothing Down or Compromised"
fDict[0] = ""
riskUtilDict[0] = 0
else:
downOrCompromisedUtils = downOrCompromisedFunctions.answers
if debug:
print "Down or Compromised Utilities:"
print downOrCompromisedUtils
#fDict[0] =
for [f,u] in downOrCompromisedUtils:
downOrCompUtil += u
riskUtilDict[0] = downOrCompUtil
for funcUtilRisk in costsPlusSortedLimited: #Limited to just ones starting from specific compromises
#u is the questionable utility, not the residual utility
if debug:
f = funcUtilRisk[4]
u = funcUtilRisk[5]
r = funcUtilRisk[6]
if r in riskUtilDict:
#If this is a new worst case scenario for that level of attacker capability
if u > riskUtilDict[r]:
riskUtilDict[r] = u
if debug:
fDict[r] = f
#This is the first time seeing this level of risk
else:
riskUtilDict[r] = u
if debug:
fDict[r] = f
if debug:
print "Worst case scenarios by attacker capability:"
print riskUtilDict
print fDict
riskUtilDictAdjusted = {}
sum = 0
worstQuestionableU = downOrCompUtil #To track the worst case so far
for r in range(0,maxRisk+1):
if r in riskUtilDict:
questionableU = riskUtilDict[r]
if questionableU > worstQuestionableU:
worstQuestionableU = questionableU
if debug:
riskUtilDictAdjusted[r] = worstQuestionableU
#Adjust the compromised components here to reflect the particular
#combination under evaluation
sum += (maxUtility - worstQuestionableU) * riskDistribution.probabilityOfRisk(r)
#print("Residual utility: " + str(sum))
if debug:
print "Adjusted worst case scenarios:"
print riskUtilDictAdjusted
print "Sum: " + str(sum)
print "Prob: " + str(prob)
sumOverCombos += sum * prob
#New code to put back in compromised components for next loop iteration
for cc in compromisedAllPossible:
if cc[0] not in compromisedComponents:
if debug:
print "Add back " + str(cc[0])
pyDatalog.assert_fact("compromised",str(cc[0]))
#End new code
if debug:
print "Residual Utility:"
#print sumOverCombos
return sumOverCombos
indirect_manager('Sam', Y)
resultado = Y.data[1]
print("resultado", resultado)
# + parent(bill, 'John Adams')
pyDatalog.assert_fact('parent', 'bill', 'John Adams')
print(pyDatalog.ask('parent(bill,X)'))
# specify what an ancestor is
pyDatalog.load("""
ancestor(X,Y) <= parent(X,Y)
ancestor(X,Y) <= parent(X,Z) & ancestor(Z,Y)
""")
# prints a set with one element : the ('bill', 'John Adams') tuple
W = pyDatalog.ask('parent(bill,X)')
print(W.answers[0][0])
#
W = pyDatalog.ask('ancestor(bill,X)')
print('Bill es ancestro de:', W.answers[0][0])
# - parent(bill, 'John Adams')
pyDatalog.retract_fact('parent', 'bill', 'John Adams')
W = pyDatalog.ask('parent(bill,X)')
if (W.__str__() != 'None'):
print(W.__str__())
else:
print('No hay resultados')
def cutNetworkConnection(ServiceA, ServiceB,CProvided,IProvided,AProvided):
pyDatalog.retract_fact("networkConnectsTo",ServiceA,ServiceB,CProvided,IProvided,AProvided)
def test():
# test of expressions
pyDatalog.load("""
+ p(a) # p is a proposition
""")
assert pyDatalog.ask('p(a)') == set([('a',)])
pyDatalog.assert_fact('p', 'a', 'b')
assert pyDatalog.ask('p(a, "b")') == set([('a', 'b')])
pyDatalog.retract_fact('p', 'a', 'b')
assert pyDatalog.ask('p(a, "b")') == None
"""unary facts """
@pyDatalog.program()
def unary():
+z()
assert ask(z()) == set([()])
+ p(a)
# check that unary queries work
assert ask(p(a)) == set([('a',)])
assert ask(p(X)) == set([('a',)])
assert ask(p(Y)) == set([('a',)])
assert ask(p(_X)) == set([('a',)])
assert ask(p(b)) == None
assert ask(p(a) & p(b)) == None
+ p(b)
assert ask(p(X), _fast=True) == set([('a',), ('b',)])
+ p(b) # facts are unique
assert ask(p(X)) == set([('a',), ('b',)])
- p(b) # retract a unary fact
assert ask(p(X)) == set([('a',)])
- p(a)
assert ask(p(X)) == None
+ p(a)
# strings and integers
+ p('c')
assert ask(p(c)) == set([('c',)])
+ p(1)
assert ask(p(1)) == set([(1,)])
+ n(None)
assert ask(n(X)) == set([(None,)])
assert ask(n(None)) == set([(None,)])
# spaces and uppercase in strings
+ farmer('Moshe dayan')
+ farmer('omar')
assert ask(farmer(X)) == set([('Moshe dayan',), ('omar',)])
# execute queries in a python program
moshe_is_a_farmer = pyDatalog.ask("farmer('Moshe dayan')")
assert moshe_is_a_farmer == set([('Moshe dayan',)])
""" binary facts """
@pyDatalog.program()
def binary():
+ q(a, b)
assert ask(q(a, b)) == set([('a', 'b')])
assert ask(q(X, b)) == set([('a', 'b')])
assert ask(q(a, Y)) == set([('a', 'b')])
assert ask(q(a, c)) == None
assert ask(q(X, Y)) == set([('a', 'b')])
+ q(a,c)
assert ask(q(a, Y)) == set([('a', 'b'), ('a', 'c')])
- q(a,c)
assert ask(q(a, Y)) == set([('a', 'b')])
assert ask(q(X, X)) == None
+q(a, a)
assert ask(q(X, X)) == set([('a', 'a')])
-q(a, a)
""" (in)equality """
@pyDatalog.program()
def equality():
assert ask(X==1) == set([(1,)])
assert ask(X==Y) == None
assert ask(X==Y+1) == None
assert ask((X==1) & (Y==1) & (X==Y)) == set([(1,1)])
assert ask((X==1) & (Y==2) & (X==Y-1)) == set([(1,2)])
#assert ask((X==1) & (Y==2) & (X+2==Y+1)) == set([(1,2)])
assert ask((X==2) & (Y==X/2)) == set([(2,1)])
assert ask((X==2) & (Y==X//2)) == set([(2,1)])
assert ask((X==1) & (Y==1+X)) == set([(1,2)])
assert ask((X==1) & (Y==1-X)) == set([(1,0)])
assert ask((X==1) & (Y==2*X)) == set([(1,2)])
assert ask((X==2) & (Y==2/X)) == set([(2,1)])
assert ask((X==2) & (Y==2//X)) == set([(2,1)])
""" Conjunctive queries """
@pyDatalog.program()
def conjuctive():
assert ask(q(X, Y) & p(X)) == set([('a', 'b')])
assert ask(p(X) & p(a)) == set([('a',),('c',),(1,)])
assert ask(p(X) & p(Y) & (X==Y)) == set([('a', 'a'), ('c', 'c'), (1, 1)])
assert ask(p(X) & p(Y) & (X==Y) & (Y==a)) == set([('a', 'a')])
assert ask(q(X, Y)) == set([('a', 'b')])
assert ask(q(X, Y) & p(X)) == set([('a', 'b')])
@pyDatalog.program()
def equality2():
assert ask((X==1) & (X<X+1)) == set([(1,)])
assert ask((X==1) & (Y==X)) == set([(1,1)])
assert ask((X==1) & (Y==X+1)) == set([(1,2)])
assert ask((X==1) & (Y==X+1) & (X<Y)) == set([(1,2)])
assert ask((X==1) & (X<1)) == None
assert ask((X==1) & (X<=1)) == set([(1,)])
assert ask((X==1) & (X>1)) == None
assert ask((X==1) & (X>=1)) == set([(1,)])
# assert ask(X==(1,2)) == set([((1,2), (1,2))])
assert ask(X in (1,)) == set([(1,)])
assert ask((X==1) & (X not in (2,))) == set([(1,)])
assert ask((X==1) & ~(X in (2,))) == set([(1,)])
assert ask((X==1) & (X not in (1,))) == None
assert ask((X==1) & ~(X in (1,))) == None
@pyDatalog.program()
def equality3():
# equality (must be between parenthesis):
s(X) <= (X == a)
assert ask(s(X)) == set([('a',)])
s(X) <= (X == 1)
assert ask(s(X)) == set([(1,), ('a',)])
s(X, Y) <= p(X) & (X == Y)
assert ask(s(a, a)) == set([('a', 'a')])
assert ask(s(a, b)) == None
assert ask(s(X,a)) == set([('a', 'a')])
assert ask(s(X, Y)) == set([('a', 'a'),('c', 'c'),(1, 1)])
assert pyDatalog.ask('p(a)') == set([('a',)])
""" clauses """
@pyDatalog.program()
def clauses():
p2(X) <= p(X)
assert ask(p2(a)) == set([('a',)])
p2(X) <= p(X)
r(X, Y) <= p(X) & p(Y)
assert ask(r(a, a)) == set([('a', 'a')])
assert ask(r(a, c)) == set([('a', 'c')])
r(X, b) <= p(X)
assert ask(r(a, b)) == set([('a', 'b')])
- (r(X, b) <= p(X))
assert ask(r(a, b)) == None
# TODO more tests
# integer variable
for i in range(10):
+ successor(i+1, i)
assert ask(successor(2, 1)) == set([(2, 1)])
# built-in
assert abs(-3)==3
assert math.sin(3)==math.sin(3)
""" in """
pyDatalog.assert_fact('is_list', (1,2))
@pyDatalog.program()
def _in():
assert ((X==1) & (X in (1,2))) == [(1,)]
_in(X) <= (X in [1,2])
assert ask(_in(1)) == set([(1,)])
assert ask(_in(9)) == None
assert ask(_in(X)) == set([(1,), (2,)])
_in2(X) <= is_list(Y) & (X in Y)
assert ask(_in2(X)) == set([(1,), (2,)])
assert ask((Y==(1,2)) & (X==1) & (X in Y)) == set([((1, 2), 1)])
assert ask((Y==(1,2)) & (X==1) & (X in Y+(3,))) == set([((1, 2), 1)])
""" recursion """
@pyDatalog.program()
def recursion():
+ even(0)
even(N) <= successor(N, N1) & odd(N1)
odd(N) <= ~ even(N)
assert ask(even(0)) == set([(0,)])
assert ask(even(X)) == set([(4,), (10,), (6,), (0,), (2,), (8,)])
assert ask(even(10)) == set([(10,)])
assert ask(odd(1)) == set([(1,)])
assert ask(odd(5)) == set([(5,)])
assert ask(even(5)) == None
""" recursion with expressions """
# reset the engine
pyDatalog.clear()
@pyDatalog.program()
def recursive_expression():
predecessor(X,Y) <= (X==Y-1)
assert ask(predecessor(X,11)) == set([(10, 11)])
p(X,Z) <= (Y==Z-1) & (X==Y-1)
assert ask(p(X,11)) == set([(9, 11)])
# odd and even
+ even(0)
even(N) <= (N > 0) & odd(N-1)
assert ask(even(0)) == set([(0,)])
odd(N) <= (N > 0) & ~ even(N)
assert ask(even(0)) == set([(0,)])
assert ask(odd(1)) == set([(1,)])
assert ask(odd(5)) == set([(5,)])
assert ask(even(5)) == None
assert ask((X==3) & odd(X+2)) == set([(3,)])
# Factorial
pyDatalog.clear()
@pyDatalog.program()
def factorial():
# (factorial[N] == F) <= (N < 1) & (F == -factorial[-N])
# + (factorial[1]==1)
# (factorial[N] == F) <= (N > 1) & (F == N*factorial[N-1])
# assert ask(factorial[1] == F) == set([(1, 1)])
# assert ask(factorial[4] == F) == set([(4, 24)])
# assert ask(factorial[-4] == F) == set([(-4, -24)])
pass
# Fibonacci
pyDatalog.clear()
@pyDatalog.program()
def fibonacci():
(fibonacci[N] == F) <= (N == 0) & (F==0)
(fibonacci[N] == F) <= (N == 1) & (F==1)
(fibonacci[N] == F) <= (N > 1) & (F == fibonacci[N-1]+fibonacci[N-2])
assert ask(fibonacci[1] == F) == set([(1, 1)])
assert ask(fibonacci[4] == F) == set([(4, 3)])
assert ask(fibonacci[18] == F) == set([(18, 2584)])
# string manipulation
@pyDatalog.program()
def _lambda():
split(X, Y,Z) <= (X == Y+'-'+Z)
assert ask(split(X, 'a', 'b')) == set([('a-b', 'a', 'b')])
split(X, Y,Z) <= (Y == (lambda X: X.split('-')[0])) & (Z == (lambda X: X.split('-')[1]))
assert ask(split('a-b', Y, Z)) == set([('a-b', 'a', 'b')])
assert ask(split(X, 'a', 'b')) == set([('a-b', 'a', 'b')])
(two[X]==Z) <= (Z==X+(lambda X: X))
assert ask(two['A']==Y) == set([('A','AA')])
""" negation """
@pyDatalog.program()
def _negation():
+p(a, b)
assert ask(~p(X, b)) == None
assert ask(~p(X, c)) == set([('X', 'c')])
pyDatalog.load("""
+ even(0)
even(N) <= (N > 0) & (N1==N-1) & odd(N1)
odd(N) <= (N2==N+2) & ~ even(N) & (N2>0)
""")
assert pyDatalog.ask('~ odd(7)', _fast=True) == None
assert pyDatalog.ask('~ odd(2)', _fast=True) == set([(2,)])
assert pyDatalog.ask('odd(3)', _fast=True) == set([(3,)])
assert pyDatalog.ask('odd(3)' ) == set([(3,)])
assert pyDatalog.ask('odd(5)', _fast=True) == set([(5,)])
assert pyDatalog.ask('odd(5)' ) == set([(5,)])
assert pyDatalog.ask('even(5)', _fast=True) == None
assert pyDatalog.ask('even(5)' ) == None
""" functions """
pyDatalog.clear()
@pyDatalog.program()
def function():
+ (f[a]==b)
assert ask(f[X]==Y) == set([('a', 'b')])
assert ask(f[X]==b) == set([('a', 'b')]) #TODO remove 'b' from result
assert ask(f[a]==X) == set([('a', 'b')])
assert ask(f[a]==b) == set([('a', 'b')])
+ (f[a]==c)
assert ask(f[a]==X) == set([('a', 'c')])
+ (f[a]==a)
assert ask(f[f[a]]==X) == set([('a',)])
assert ask(f[X]==f[a]) == set([('a',)])
assert ask(f[X]==f[a]+'') == set([('a',)])
- (f[a]==a)
assert ask(f[f[a]]==X) == None
+ (f[a]==None)
assert (ask(f[a]==X)) == set([('a',None)])
+ (f[a]==(1,2))
assert (ask(f[a]==X)) == set([('a',(1,2))])
assert (ask(f[X]==(1,2))) == set([('a',(1,2))])
+ (f[a]==c)
+ (f2[a,x]==b)
assert ask(f2[a,x]==b) == set([('a', 'x', 'b')])
+ (f2[a,x]==c)
assert ask(f2[a,x]==X) == set([('a', 'x', 'c')])
g[X] = f[X]+f[X]
assert(ask(g[a]==X)) == set([('a', 'cc')])
h(X,Y) <= (f[X]==Y)
assert (ask(h(X,'c'))) == set([('a', 'c')])
assert (ask(h(X,Y))) == set([('a', 'c')])
@pyDatalog.program()
def function_comparison():
assert ask(f[X]==Y) == set([('a', 'c')])
assert ask(f[a]<'d') == set([('c',)])
assert ask(f[a]>'a') == set([('c',)])
assert ask(f[a]>='c') == set([('c',)])
assert ask(f[a]>'c') == None
assert ask(f[a]<='c') == set([('c',)])
assert ask(f[a]>'c') == None
assert ask(f[a] in ['c',]) == set([('c',)])
assert ask((f[X]=='c') & (f[Y]==f[X])) == set([('a', 'a')])
assert ask((f[X]=='c') & (f[Y]==f[X]+'')) == set([('a', 'a')])
assert ask((f[X]=='c') & (f[Y]==(lambda X : 'c'))) == set([('a', 'a')])
assert ask(f[X]==Y+'') == None
assert ask((Y=='c') &(f[X]==Y+'')) == set([('c', 'a')])
assert ask((Y=='c') &(f[X]<=Y+'')) == set([('c', 'a')])
assert ask((Y=='c') &(f[X]<Y+'')) == None
assert ask((Y=='c') &(f[X]<'d'+Y+'')) == set([('c', 'a')])
assert ask((Y==('a','c')) & (f[X] in Y)) == set([(('a', 'c'), 'a')])
assert ask((Y==('a','c')) & (f[X] in (Y+('z',)))) == set([(('a', 'c'), 'a')])
assert ask(f[X]==f[X]+'') == set([('a',)])
@pyDatalog.program()
def function_negation():
assert not(ask(~(f[a]<'d')))
assert not(ask(~(f[X]<'d')))
assert ask(~(f[a] in('d',)))
""" aggregates """
pyDatalog.clear()
@pyDatalog.program()
def sum():
+ p(a, c, 1)
+ p(b, b, 4)
+ p(a, b, 1)
assert(sum(1,2)) == 3
(a_sum[X] == sum(Y, key=Z)) <= p(X, Z, Y)
assert ask(a_sum[X]==Y) == set([('a', 2), ('b', 4)])
assert ask(a_sum[a]==X) == set([('a', 2)])
assert ask(a_sum[a]==2) == set([('a', 2)])
assert ask(a_sum[X]==4) == set([('b', 4)])
assert ask(a_sum[c]==X) == None
assert ask((a_sum[X]==2) & (p(X, Z, Y))) == set([('a', 'c', 1), ('a', 'b', 1)])
(a_sum2[X] == sum(Y, for_each=X)) <= p(X, Z, Y)
assert ask(a_sum2[a]==X) == set([('a', 1)])
(a_sum3[X] == sum(Y, key=(X,Z))) <= p(X, Z, Y)
assert ask(a_sum3[X]==Y) == set([('a', 2), ('b', 4)])
assert ask(a_sum3[a]==X) == set([('a', 2)])
@pyDatalog.program()
def len():
assert(len((1,2))) == 2
(a_len[X] == len(Z)) <= p(X, Z, Y)
assert ask(a_len[X]==Y) == set([('a', 2), ('b', 1)])
assert ask(a_len[a]==X) == set([('a', 2)])
assert ask(a_len[X]==1) == set([('b', 1)])
assert ask(a_len[X]==5) == None
(a_lenY[X] == len(Y)) <= p(X, Z, Y)
assert ask(a_lenY[a]==X) == set([('a', 1)])
assert ask(a_lenY[c]==X) == None
(a_len2[X,Y] == len(Z)) <= p(X, Y, Z)
assert ask(a_len2[a,b]==X) == set([('a', 'b', 1)])
assert ask(a_len2[a,X]==Y) == set([('a', 'b', 1), ('a', 'c', 1)])
+ q(a, c, 1)
+ q(a, b, 2)
+ q(b, b, 4)
@pyDatalog.program()
def concat():
(a_concat[X] == concat(Y, key=Z, sep='+')) <= q(X, Y, Z)
assert ask(a_concat[X]==Y) == set([('b', 'b'), ('a', 'c+b')])
assert ask(a_concat[a]=='c+b') == set([('a', 'c+b')])
assert ask(a_concat[a]==X) == set([('a', 'c+b')])
assert ask(a_concat[X]==b) == set([('b', 'b')])
(a_concat2[X] == concat(Y, order_by=(Z,), sep='+')) <= q(X, Y, Z)
assert ask(a_concat2[a]==X) == set([('a', 'c+b')])
(a_concat3[X] == concat(Y, key=(-Z,), sep='-')) <= q(X, Y, Z)
assert ask(a_concat3[a]==X) == set([('a', 'b-c')])
@pyDatalog.program()
def min():
assert min(1,2) == 1
(a_min[X] == min(Y, key=Z)) <= q(X, Y, Z)
assert ask(a_min[X]==Y) == set([('b', 'b'), ('a', 'c')])
assert ask(a_min[a]=='c') == set([('a', 'c')])
assert ask(a_min[a]==X) == set([('a', 'c')])
assert ask(a_min[X]=='b') == set([('b', 'b')])
(a_minD[X] == min(Y, order_by=-Z)) <= q(X, Y, Z)
assert ask(a_minD[a]==X) == set([('a', 'b')])
(a_min2[X, Y] == min(Z, key=(X,Y))) <= q(X, Y, Z)
assert ask(a_min2[Y, b]==X) == set([('a', 'b', 2),('b', 'b', 4)])
assert ask(a_min2[Y, Y]==X) == set([('b', 'b', 4)]), "a_min2"
(a_min3[Y] == min(Z, key=(-X,Z))) <= q(X, Y, Z)
assert ask(a_min3[b]==Y) == set([('b', 4)]), "a_min3"
@pyDatalog.program()
def max():
assert max(1,2) == 2
(a_max[X] == max(Y, key=-Z)) <= q(X, Y, Z)
assert ask(a_max[a]==X) == set([('a', 'c')])
(a_maxD[X] == max(Y, order_by=Z)) <= q(X, Y, Z)
assert ask(a_maxD[a]==X) == set([('a', 'b')])
@pyDatalog.program()
def rank():
(a_rank1[Z] == rank(for_each=Z, order_by=Z)) <= q(X, Y, Z)
assert ask(a_rank1[X]==Y) == set([(1, 0), (2, 0), (4, 0)])
assert ask(a_rank1[X]==0) == set([(1, 0), (2, 0), (4, 0)])
assert ask(a_rank1[1]==X) == set([(1, 0)])
assert ask(a_rank1[1]==0) == set([(1, 0)])
assert ask(a_rank1[1]==1) == None
# rank
(a_rank[X,Y] == rank(for_each=(X,Y2), order_by=Z2)) <= q(X, Y, Z) & q(X,Y2,Z2)
assert ask(a_rank[X,Y]==Z) == set([('a', 'b', 1), ('a', 'c', 0), ('b', 'b', 0)])
assert ask(a_rank[a,b]==1) == set([('a', 'b', 1)])
assert ask(a_rank[a,b]==Y) == set([('a', 'b', 1)])
assert ask(a_rank[a,X]==0) == set([('a', 'c', 0)])
assert ask(a_rank[a,X]==Y) == set([('a', 'b', 1), ('a', 'c', 0)])
assert ask(a_rank[X,Y]==1) == set([('a', 'b', 1)])
assert ask(a_rank[a,y]==Y) == None
# reversed
(b_rank[X,Y] == rank(for_each=(X,Y2), order_by=-Z2)) <= q(X, Y, Z) & q(X,Y2,Z2)
assert ask(b_rank[X,Y]==Z) == set([('a', 'b', 0), ('a', 'c', 1), ('b', 'b', 0)])
assert ask(b_rank[a,b]==0) == set([('a', 'b', 0)])
assert ask(b_rank[a,b]==Y) == set([('a', 'b', 0)])
assert ask(b_rank[a,X]==1) == set([('a', 'c', 1)])
assert ask(b_rank[a,X]==Y) == set([('a', 'b', 0), ('a', 'c', 1)])
assert ask(b_rank[X,Y]==0) == set([('a', 'b', 0), ('b', 'b', 0)])
assert ask(b_rank[a,y]==Y) == None
@pyDatalog.program()
def running_sum():
# running_sum
(a_run_sum[X,Y] == running_sum(Z2, for_each=(X,Y2), order_by=Z2)) <= q(X, Y, Z) & q(X,Y2,Z2)
assert ask(a_run_sum[X,Y]==Z) == set([('a', 'b', 3), ('a', 'c', 1), ('b', 'b', 4)])
assert ask(a_run_sum[a,b]==3) == set([('a', 'b', 3)])
assert ask(a_run_sum[a,b]==Y) == set([('a', 'b', 3)])
assert ask(a_run_sum[a,X]==1) == set([('a', 'c', 1)])
assert ask(a_run_sum[a,X]==Y) == set([('a', 'b', 3), ('a', 'c', 1)])
assert ask(a_run_sum[X,Y]==4) == set([('b', 'b', 4)])
assert ask(a_run_sum[a,y]==Y) == None
(b_run_sum[X,Y] == running_sum(Z2, for_each=(X,Y2), order_by=-Z2)) <= q(X, Y, Z) & q(X,Y2,Z2)
assert ask(b_run_sum[X,Y]==Z) == set([('a', 'b', 2), ('a', 'c', 3), ('b', 'b', 4)])
assert ask(b_run_sum[a,b]==2) == set([('a', 'b', 2)])
assert ask(b_run_sum[a,b]==Y) == set([('a', 'b', 2)])
assert ask(b_run_sum[a,X]==3) == set([('a', 'c', 3)])
assert ask(b_run_sum[a,X]==Y) == set([('a', 'b', 2), ('a', 'c', 3)])
assert ask(b_run_sum[X,Y]==4) == set([('b', 'b', 4)])
assert ask(b_run_sum[a,y]==Y) == None
""" simple in-line queries """
X = pyDatalog.Variable()
assert ((X==1) >= X) == 1
assert ((X==1) & (X!=2) >= X) == 1
assert set(X._in((1,2))) == set([(1,),(2,)])
assert ((X==1) & (X._in ((1,2)))) == [(1,)]
""" interface with python classes """
class A(pyDatalog.Mixin):
def __init__(self, b):
super(A, self).__init__()
self.b = b
def __repr__(self):
return self.b
@pyDatalog.program() # indicates that the following method contains pyDatalog clauses
def _():
(A.c[X]==N) <= (A.b[X]==N)
(A.len[X]==len(N)) <= (A.b[X]==N)
@classmethod
def _pyD_x1(cls, X):
if X.is_const() and X.id.b == 'za':
yield (X.id,)
else:
for X in pyDatalog.metaMixin.__refs__[cls]:
if X.b == 'za':
yield (X,)
a = A('a')
b = A('b')
assert a.c == 'a'
X, Y = pyDatalog.variables(2)
assert (A.c[X]=='a') == [(a,)]
assert (A.c[X]=='a')[0] == (a,)
assert list(X.data) == [a]
assert X.v() == a
assert ((A.c[a]==X) >= X) == 'a'
assert ((A.c[a]==X) & (A.c[a]==X) >= X) == 'a'
assert ((A.c[a]==X) & (A.c[b]==X) >= X) == None
(A.c[X]=='b') & (A.b[X]=='a')
assert list(X.data) == []
(A.c[X]=='a') & (A.b[X]=='a')
assert list(X.data) == [a]
result = (A.c[X]=='a') & (A.b[X]=='a')
assert result == [(a,)]
assert (A.c[a] == 'a') == [()]
assert (A.b[a] == 'a') == [()]
assert (A.c[a]=='a') & (A.b[a]=='a') == [()]
assert (A.b[a]=='f') == []
assert ((A.c[a]=='a') & (A.b[a]=='f')) == []
""" filters on python classes """
assert (A.b[X]!=Y) == [(a, None), (b, None)]
assert (A.b[X]!='a') == [(b,)]
assert (A.b[X]!='z') == [(a,), (b,)]
assert (A.b[a]!='a') == []
assert list(A.b[b]!='a') == [()]
assert ((A.b[b]!='a') & (A.b[b]!='z')) == [()]
assert (A.b[X]<Y) == [(a, None), (b, None)]
assert (A.b[X]<'a') == []
assert (A.b[X]<'z') == [(a,), (b,)]
assert (A.b[a]<'b') == [()]
assert (A.b[b]<'a') == []
assert ((A.b[b]<'z') & (A.b[b]!='z')) == [()]
assert (A.b[X]<='a') == [(a,)]
assert (A.b[X]<='z') == [(a,), (b,)]
assert (A.b[a]<='b') == [()]
assert (A.b[b]<='a') == []
assert ((A.b[b]<='z') & (A.b[b]!='z')) == [()]
assert (A.b[X]>'a') == [(b,)]
assert (A.b[X]>='a') == [(a,), (b,)]
assert (A.c[X]<='a') == [(a,)]
assert (A.c[X]<='a'+'') == [(a,)]
assert (A.c[X]._in(('a',))) == [(a,)]
assert (A.c[X]._in(('a',)+('z',))) == [(a,)]
assert ((Y==('a',)) & (A.c[X]._in(Y))) == [(('a',), a)] # TODO make ' in ' work
assert ((Y==('a',)) & (A.c[X]._in(Y+('z',)))) == [(('a',), a)] # TODO make ' in ' work
assert (A.c[X]._in(('z',))) == []
# more complex queries
assert ((Y=='a') & (A.b[X]!=Y)) == [('a', b)] # order of appearance of the variables !
assert (A.len[X]==Y) == [(b, 1), (a, 1)]
assert (A.len[a]==Y) == [(1,)]
""" subclass """
class Z(A):
def __init__(self, z):
super(Z, self).__init__(z+'a')
self.z = z
def __repr__(self):
return self.z
@pyDatalog.program() # indicates that the following method contains pyDatalog clauses
def _():
(Z.w[X]==N) <= (Z.z[X]!=N)
@classmethod
def _pyD_query(cls, pred_name, args):
if pred_name == 'Z.pred':
if args[0].is_const() and args[0].id.b != 'za':
yield (args[0].id,)
else:
for X in pyDatalog.metaMixin.__refs__[cls]:
if X.b != 'za':
yield (X,)
else:
raise AttributeError
z = Z('z')
assert z.z == 'z'
assert (Z.z[X]=='z') == [(z,)]
assert ((Z.z[X]=='z') & (Z.z[X]>'a')) == [(z,)]
assert list(X.data) == [z]
try:
a.z == 'z'
except Exception as e:
e_message = e.message if hasattr(e, 'message') else e.args[0]
if e_message != "Predicate without definition (or error in resolver): A.z[1]==/2":
print(e_message)
else:
assert False
try:
(Z.z[a] == 'z') == None
except Exception as e:
e_message = e.message if hasattr(e, 'message') else e.args[0]
if e_message != "Object is incompatible with the class that is queried.":
print(e_message)
else:
assert False
assert (Z.b[X]==Y) == [(z, 'za')]
assert (Z.c[X]==Y) == [(z, 'za')]
assert ((Z.c[X]==Y) & (Z.c[X]>'a')) == [(z, 'za')]
assert (Z.c[X]>'a') == [(z,)]
assert ((Z.c[X]>'a') & (A.c[X]=='za')) == [(z,)]
assert (A.c[X]=='za') == [(z,)]
assert (A.c[z]=='za') == [()]
assert (z.b) == 'za'
assert (z.c) == 'za'
w = Z('w')
w = Z('w') # duplicated to test __refs__[cls]
assert(Z.x(X)) == [(z,)]
assert not (~Z.x(z))
assert ~Z.x(w)
assert ~ (Z.z[w]=='z')
assert(Z.pred(X)) == [(w,)] # not duplicated !
assert(Z.pred(X) & ~ (Z.z[X]>='z')) == [(w,)]
assert(Z.x(X) & ~(Z.pred(X))) == [(z,)]
assert (Z.len[X]==Y) == [(w, 1), (z, 1)]
assert (Z.len[z]==Y) == [(1,)]
# TODO print (A.b[w]==Y)
""" python resolvers """
@pyDatalog.predicate()
def p(X,Y):
yield (1,2)
yield (2,3)
assert pyDatalog.ask('p(X,Y)') == set([(1, 2), (2, 3)])
assert pyDatalog.ask('p(1,Y)') == set([(1, 2)])
assert pyDatalog.ask('p(1,2)') == set([(1, 2)])
""" error detection """
@pyDatalog.program()
def _():
pass
error = False
try:
_()
except: error = True
assert error
def assert_error(code, message='^$'):
_error = False
try:
pyDatalog.load(code)
except Exception as e:
e_message = e.message if hasattr(e, 'message') else e.args[0] # python 2 and 3
if not re.match(message, e_message):
print(e_message)
_error = True
assert _error
def assert_ask(code, message='^$'):
_error = False
try:
pyDatalog.ask(code)
except Exception as e:
e_message = e.message if hasattr(e, 'message') else e.args[0]
if not re.match(message, e_message):
print(e_message)
_error = True
assert _error
assert_error('ask(z(a),True)', 'Too many arguments for ask \!')
assert_error('ask(z(a))', 'Predicate without definition \(or error in resolver\): z/1')
assert_error("+ farmer(farmer(moshe))", "Syntax error: Literals cannot have a literal as argument : farmer\[\]")
assert_error("+ manager[Mary]==John", "Left-hand side of equality must be a symbol or function, not an expression.")
assert_error("manager[X]==Y <= (X==Y)", "Syntax error: please verify parenthesis around \(in\)equalities")
assert_error("p(X) <= (Y==2)", "Can't create clause")
assert_error("p(X) <= X==1 & X==2", "Syntax error: please verify parenthesis around \(in\)equalities")
assert_error("p(X) <= (manager[X]== min(X))", "Error: argument missing in aggregate")
assert_error("p(X) <= (manager[X]== max(X, order_by=X))", "Aggregation cannot appear in the body of a clause")
assert_error("q(min(X, order_by=X)) <= p(X)", "Syntax error: Incorrect use of aggregation\.")
assert_error("manager[X]== min(X, order_by=X) <= manager(X)", "Syntax error: please verify parenthesis around \(in\)equalities")
assert_error("(manager[X]== min(X, order_by=X+2)) <= manager(X)", "order_by argument of aggregate must be variable\(s\), not expression\(s\).")
assert_error("ask(X<1)", 'Error: left hand side of comparison must be bound: =X<1/1')
assert_error("ask(X<Y)", 'Error: left hand side of comparison must be bound: =X<Y/2')
assert_error("ask(1<Y)", 'Error: left hand side of comparison must be bound: =Y>1/1')
assert_error("ask( (A.c[X]==Y) & (Z.c[X]==Y))", "TypeError: First argument of Z.c\[1\]==\('.','.'\) must be a Z, not a A ")
assert_ask("A.u[X]==Y", "Predicate without definition \(or error in resolver\): A.u\[1\]==/2")
assert_ask("A.u[X,Y]==Z", "Predicate without definition \(or error in resolver\): A.u\[2\]==/3")
assert_error('(a_sum[X] == sum(Y, key=Y)) <= p(X, Z, Y)', "Error: Duplicate definition of aggregate function.")
assert_error('(two(X)==Z) <= (Z==X+(lambda X: X))', 'Syntax error near equality: consider using brackets. two\(X\)')
assert_error('p(X) <= sum(X, key=X)', 'Invalid body for clause')
assert_error('ask(- manager[X]==1)', "Left-hand side of equality must be a symbol or function, not an expression.")
assert_error("p(X) <= (X=={})", "unhashable type: 'dict'")
""" SQL Alchemy """
from sqlalchemy import create_engine
from sqlalchemy import Column, Integer, String, ForeignKey
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker, relationship
engine = create_engine('sqlite:///:memory:', echo=False) # create database in memory
Session = sessionmaker(bind=engine)
session = Session()
Base = declarative_base(cls=pyDatalog.Mixin, metaclass=pyDatalog.sqlMetaMixin)
Base.session = session
class Employee(Base): # --> Employee inherits from the Base class
__tablename__ = 'employee'
name = Column(String, primary_key=True)
manager_name = Column(String, ForeignKey('employee.name'))
salary = Column(Integer)
def __init__(self, name, manager_name, salary):
super(Employee, self).__init__()
self.name = name
self.manager_name = manager_name # direct manager of the employee, or None
self.salary = salary # monthly salary of the employee
def __repr__(self): # specifies how to display the employee
return "Employee: %s" % self.name
@pyDatalog.program() # --> the following function contains pyDatalog clauses
def Employee():
(Employee.manager[X]==Y) <= (Employee.manager_name[X]==Z) & (Z==Employee.name[Y])
# the salary class of employee X is computed as a function of his/her salary
# this statement is a logic equality, not an assignment !
Employee.salary_class[X] = Employee.salary[X]//1000
# all the indirect managers of employee X are derived from his manager, recursively
Employee.indirect_manager(X,Y) <= (Employee.manager[X]==Y) & (Y != None)
Employee.indirect_manager(X,Y) <= (Employee.manager[X]==Z) & Employee.indirect_manager(Z,Y) & (Y != None)
# count the number of reports of X
(Employee.report_count[X] == len(Y)) <= Employee.indirect_manager(Y,X)
Employee.p(X,Y) <= (Y <= Employee.salary[X] + 1)
Base.metadata.create_all(engine)
John = Employee('John', None, 6800)
Mary = Employee('Mary', 'John', 6300)
Sam = Employee('Sam', 'Mary', 5900)
session.add(John)
session.add(Mary)
session.add(Sam)
session.commit()
assert (John.salary_class ==6)
X = pyDatalog.Variable()
result = (Employee.salary[X] == 6300) # notice the similarity to a pyDatalog query
assert result == [(Mary,), ]
assert (X._value() == [Mary,]) # prints [Employee: Mary]
assert (X.v() == Mary) # prints Employee:Mary
result = (Employee.indirect_manager(Mary, X))
assert result == [(John,), ]
assert (X.v() == John) # prints [Employee: John]
Mary.salary_class = ((Employee.salary_class[Mary]==X) >= X)
Mary.salary = 10000
assert Mary.salary_class != ((Employee.salary_class[Mary]==X) >= X)
X, Y, N = pyDatalog.variables(3)
result = (Employee.salary[X]==6800) & (Employee.name[X]==N)
assert result == [(John,'John'), ]
assert N.v() == 'John'
result = (Employee.salary[X]==Employee.salary[X])
assert result == [(John,), (Mary,), (Sam,)]
result = (Employee.p(X,1))
assert result == [(John,), (Mary,), (Sam,)]
result = (Employee.salary[X]<Employee.salary[X]+1)
assert result == [(John,), (Mary,), (Sam,)]
result = (Employee.salary[John]==N) & Employee.p(John, N)
assert result == [(6800,)]
result = (Employee.salary[X]==6800) & (Employee.salary[X]==N) & Employee.p(X, N)
assert result == [(John, 6800)]
"""
def mitigate(ServiceA):
pyDatalog.retract_fact(compromised,ServiceA)
def __retract_user(self, user, prediction):
retract_fact('user_id', user.id)
retract_fact('name', user.id, user.name)
retract_fact('age', user.id, user.age)
retract_fact('bio', user.id, user.bio)
retract_fact('distance', user.id, user.distance)
retract_fact('distance_unit', user.id, user.distance_unit)
retract_fact('ping_time', user.id, user.ping_time)
retract_fact('birth_date', user.id, user.birth_date)
for friend in user.common_friends:
retract_fact('common_friend', user.id, friend)
for like in user.common_likes:
retract_fact('common_likes', user.id, like)
retract_fact('prediction', user.id, prediction)
def __retract_user(self, user, prediction):
retract_fact('user_id', user.id)
retract_fact('name', user.id, user.name)
retract_fact('age', user.id, user.age)
retract_fact('bio', user.id, user.bio)
retract_fact('distance', user.id, user.distance)
retract_fact('distance_unit', user.id, user.distance_unit)
retract_fact('ping_time', user.id, user.ping_time)
retract_fact('birth_date', user.id, user.birth_date)
for friend in user.common_friends:
retract_fact('common_friend', user.id, friend)
for like in user.common_likes:
retract_fact('common_likes', user.id, like)
retract_fact('prediction', user.id, prediction)
def borrar_sintomas(sintoma):
for i in sintoma(X):
pyDatalog.retract_fact(str(sintoma),str(i[0]))
def cutNetworkConnection(ServiceA, ServiceB):
pyDatalog.retract_fact("networkConnectsTo",ServiceA,ServiceB)
def moveService(ServiceA,HostA,HostB):
pyDatalog.retract_fact(residesOn,ServiceA,HostA)
pyDatalog.assert_fact(residesOn,HostB)
def test():
# test of expressions
pyDatalog.load("""
+ p(a) # p is a proposition
""")
assert pyDatalog.ask('p(a)') == set([('a', )])
pyDatalog.assert_fact('p', 'a', 'b')
assert pyDatalog.ask('p(a, "b")') == set([('a', 'b')])
pyDatalog.retract_fact('p', 'a', 'b')
assert pyDatalog.ask('p(a, "b")') == None
"""unary facts """
@pyDatalog.program()
def unary():
+z()
assert ask(z()) == set([()])
+p(a)
# check that unary queries work
assert ask(p(a)) == set([('a', )])
assert ask(p(X)) == set([('a', )])
assert ask(p(Y)) == set([('a', )])
assert ask(p(_X)) == set([('a', )])
assert ask(p(b)) == None
assert ask(p(a) & p(b)) == None
+p(b)
assert ask(p(X), _fast=True) == set([('a', ), ('b', )])
+p(b) # facts are unique
assert ask(p(X)) == set([('a', ), ('b', )])
-p(b) # retract a unary fact
assert ask(p(X)) == set([('a', )])
-p(a)
assert ask(p(X)) == None
+p(a)
# strings and integers
+p('c')
assert ask(p(c)) == set([('c', )])
+p(1)
assert ask(p(1)) == set([(1, )])
+n(None)
assert ask(n(X)) == set([(None, )])
assert ask(n(None)) == set([(None, )])
# spaces and uppercase in strings
+farmer('Moshe dayan')
+farmer('omar')
assert ask(farmer(X)) == set([('Moshe dayan', ), ('omar', )])
# execute queries in a python program
moshe_is_a_farmer = pyDatalog.ask("farmer('Moshe dayan')")
assert moshe_is_a_farmer == set([('Moshe dayan', )])
""" binary facts """
@pyDatalog.program()
def binary():
+q(a, b)
assert ask(q(a, b)) == set([('a', 'b')])
assert ask(q(X, b)) == set([('a', 'b')])
assert ask(q(a, Y)) == set([('a', 'b')])
assert ask(q(a, c)) == None
assert ask(q(X, Y)) == set([('a', 'b')])
+q(a, c)
assert ask(q(a, Y)) == set([('a', 'b'), ('a', 'c')])
-q(a, c)
assert ask(q(a, Y)) == set([('a', 'b')])
assert ask(q(X, X)) == None
+q(a, a)
assert ask(q(X, X)) == set([('a', 'a')])
-q(a, a)
""" (in)equality """
@pyDatalog.program()
def equality():
assert ask(X == 1) == set([(1, )])
assert ask(X == Y) == None
assert ask(X == Y + 1) == None
assert ask((X == 1) & (Y == 1) & (X == Y)) == set([(1, 1)])
assert ask((X == 1) & (Y == 2) & (X == Y - 1)) == set([(1, 2)])
#assert ask((X==1) & (Y==2) & (X+2==Y+1)) == set([(1,2)])
assert ask((X == 2) & (Y == X / 2)) == set([(2, 1)])
assert ask((X == 2) & (Y == X // 2)) == set([(2, 1)])
assert ask((X == 1) & (Y == 1 + X)) == set([(1, 2)])
assert ask((X == 1) & (Y == 1 - X)) == set([(1, 0)])
assert ask((X == 1) & (Y == 2 * X)) == set([(1, 2)])
assert ask((X == 2) & (Y == 2 / X)) == set([(2, 1)])
assert ask((X == 2) & (Y == 2 // X)) == set([(2, 1)])
""" Conjunctive queries """
@pyDatalog.program()
def conjuctive():
assert ask(q(X, Y) & p(X)) == set([('a', 'b')])
assert ask(p(X) & p(a)) == set([('a', ), ('c', ), (1, )])
assert ask(p(X) & p(Y) & (X == Y)) == set([('a', 'a'), ('c', 'c'),
(1, 1)])
assert ask(p(X) & p(Y) & (X == Y) & (Y == a)) == set([('a', 'a')])
assert ask(q(X, Y)) == set([('a', 'b')])
assert ask(q(X, Y) & p(X)) == set([('a', 'b')])
@pyDatalog.program()
def equality2():
assert ask((X == 1) & (X < X + 1)) == set([(1, )])
assert ask((X == 1) & (Y == X)) == set([(1, 1)])
assert ask((X == 1) & (Y == X + 1)) == set([(1, 2)])
assert ask((X == 1) & (Y == X + 1) & (X < Y)) == set([(1, 2)])
assert ask((X == 1) & (X < 1)) == None
assert ask((X == 1) & (X <= 1)) == set([(1, )])
assert ask((X == 1) & (X > 1)) == None
assert ask((X == 1) & (X >= 1)) == set([(1, )])
# assert ask(X==(1,2)) == set([((1,2), (1,2))])
assert ask(X in (1, )) == set([(1, )])
assert ask((X == 1) & (X not in (2, ))) == set([(1, )])
assert ask((X == 1) & ~(X in (2, ))) == set([(1, )])
assert ask((X == 1) & (X not in (1, ))) == None
assert ask((X == 1) & ~(X in (1, ))) == None
@pyDatalog.program()
def equality3():
# equality (must be between parenthesis):
s(X) <= (X == a)
assert ask(s(X)) == set([('a', )])
s(X) <= (X == 1)
assert ask(s(X)) == set([(1, ), ('a', )])
s(X, Y) <= p(X) & (X == Y)
assert ask(s(a, a)) == set([('a', 'a')])
assert ask(s(a, b)) == None
assert ask(s(X, a)) == set([('a', 'a')])
assert ask(s(X, Y)) == set([('a', 'a'), ('c', 'c'), (1, 1)])
assert pyDatalog.ask('p(a)') == set([('a', )])
""" clauses """
@pyDatalog.program()
def clauses():
p2(X) <= p(X)
assert ask(p2(a)) == set([('a', )])
p2(X) <= p(X)
r(X, Y) <= p(X) & p(Y)
assert ask(r(a, a)) == set([('a', 'a')])
assert ask(r(a, c)) == set([('a', 'c')])
r(X, b) <= p(X)
assert ask(r(a, b)) == set([('a', 'b')])
-(r(X, b) <= p(X))
assert ask(r(a, b)) == None
# TODO more tests
# integer variable
for i in range(10):
+successor(i + 1, i)
assert ask(successor(2, 1)) == set([(2, 1)])
# built-in
assert abs(-3) == 3
assert math.sin(3) == math.sin(3)
""" in """
pyDatalog.assert_fact('is_list', (1, 2))
@pyDatalog.program()
def _in():
assert ((X == 1) & (X in (1, 2))) == [(1, )]
_in(X) <= (X in [1, 2])
assert ask(_in(1)) == set([(1, )])
assert ask(_in(9)) == None
assert ask(_in(X)) == set([(1, ), (2, )])
_in2(X) <= is_list(Y) & (X in Y)
assert ask(_in2(X)) == set([(1, ), (2, )])
assert ask((Y == (1, 2)) & (X == 1) & (X in Y)) == set([((1, 2), 1)])
assert ask((Y == (1, 2)) & (X == 1) & (X in Y + (3, ))) == set([
((1, 2), 1)
])
""" recursion """
@pyDatalog.program()
def recursion():
+even(0)
even(N) <= successor(N, N1) & odd(N1)
odd(N) <= ~even(N)
assert ask(even(0)) == set([(0, )])
assert ask(even(X)) == set([(4, ), (10, ), (6, ), (0, ), (2, ), (8, )])
assert ask(even(10)) == set([(10, )])
assert ask(odd(1)) == set([(1, )])
assert ask(odd(5)) == set([(5, )])
assert ask(even(5)) == None
""" recursion with expressions """
# reset the engine
pyDatalog.clear()
@pyDatalog.program()
def recursive_expression():
predecessor(X, Y) <= (X == Y - 1)
assert ask(predecessor(X, 11)) == set([(10, 11)])
p(X, Z) <= (Y == Z - 1) & (X == Y - 1)
assert ask(p(X, 11)) == set([(9, 11)])
# odd and even
+even(0)
even(N) <= (N > 0) & odd(N - 1)
assert ask(even(0)) == set([(0, )])
odd(N) <= (N > 0) & ~even(N)
assert ask(even(0)) == set([(0, )])
assert ask(odd(1)) == set([(1, )])
assert ask(odd(5)) == set([(5, )])
assert ask(even(5)) == None
assert ask((X == 3) & odd(X + 2)) == set([(3, )])
# Factorial
pyDatalog.clear()
@pyDatalog.program()
def factorial():
# (factorial[N] == F) <= (N < 1) & (F == -factorial[-N])
# + (factorial[1]==1)
# (factorial[N] == F) <= (N > 1) & (F == N*factorial[N-1])
# assert ask(factorial[1] == F) == set([(1, 1)])
# assert ask(factorial[4] == F) == set([(4, 24)])
# assert ask(factorial[-4] == F) == set([(-4, -24)])
pass
# Fibonacci
pyDatalog.clear()
@pyDatalog.program()
def fibonacci():
(fibonacci[N] == F) <= (N == 0) & (F == 0)
(fibonacci[N] == F) <= (N == 1) & (F == 1)
(fibonacci[N]
== F) <= (N > 1) & (F == fibonacci[N - 1] + fibonacci[N - 2])
assert ask(fibonacci[1] == F) == set([(1, 1)])
assert ask(fibonacci[4] == F) == set([(4, 3)])
assert ask(fibonacci[18] == F) == set([(18, 2584)])
# string manipulation
@pyDatalog.program()
def _lambda():
split(X, Y, Z) <= (X == Y + '-' + Z)
assert ask(split(X, 'a', 'b')) == set([('a-b', 'a', 'b')])
split(X, Y, Z) <= (Y == (lambda X: X.split('-')[0])) & (Z == (
lambda X: X.split('-')[1]))
assert ask(split('a-b', Y, Z)) == set([('a-b', 'a', 'b')])
assert ask(split(X, 'a', 'b')) == set([('a-b', 'a', 'b')])
(two[X] == Z) <= (Z == X + (lambda X: X))
assert ask(two['A'] == Y) == set([('A', 'AA')])
""" negation """
@pyDatalog.program()
def _negation():
+p(a, b)
assert ask(~p(X, b)) == None
assert ask(~p(X, c)) == set([('X', 'c')])
pyDatalog.load("""
+ even(0)
even(N) <= (N > 0) & (N1==N-1) & odd(N1)
odd(N) <= (N2==N+2) & ~ even(N) & (N2>0)
""")
assert pyDatalog.ask('~ odd(7)', _fast=True) == None
assert pyDatalog.ask('~ odd(2)', _fast=True) == set([(2, )])
assert pyDatalog.ask('odd(3)', _fast=True) == set([(3, )])
assert pyDatalog.ask('odd(3)') == set([(3, )])
assert pyDatalog.ask('odd(5)', _fast=True) == set([(5, )])
assert pyDatalog.ask('odd(5)') == set([(5, )])
assert pyDatalog.ask('even(5)', _fast=True) == None
assert pyDatalog.ask('even(5)') == None
""" functions """
pyDatalog.clear()
@pyDatalog.program()
def function():
+(f[a] == b)
assert ask(f[X] == Y) == set([('a', 'b')])
assert ask(f[X] == b) == set([('a', 'b')
]) #TODO remove 'b' from result
assert ask(f[a] == X) == set([('a', 'b')])
assert ask(f[a] == b) == set([('a', 'b')])
+(f[a] == c)
assert ask(f[a] == X) == set([('a', 'c')])
+(f[a] == a)
assert ask(f[f[a]] == X) == set([('a', )])
assert ask(f[X] == f[a]) == set([('a', )])
assert ask(f[X] == f[a] + '') == set([('a', )])
-(f[a] == a)
assert ask(f[f[a]] == X) == None
+(f[a] == None)
assert (ask(f[a] == X)) == set([('a', None)])
+(f[a] == (1, 2))
assert (ask(f[a] == X)) == set([('a', (1, 2))])
assert (ask(f[X] == (1, 2))) == set([('a', (1, 2))])
+(f[a] == c)
+(f2[a, x] == b)
assert ask(f2[a, x] == b) == set([('a', 'x', 'b')])
+(f2[a, x] == c)
assert ask(f2[a, x] == X) == set([('a', 'x', 'c')])
g[X] = f[X] + f[X]
assert (ask(g[a] == X)) == set([('a', 'cc')])
h(X, Y) <= (f[X] == Y)
assert (ask(h(X, 'c'))) == set([('a', 'c')])
assert (ask(h(X, Y))) == set([('a', 'c')])
@pyDatalog.program()
def function_comparison():
assert ask(f[X] == Y) == set([('a', 'c')])
assert ask(f[a] < 'd') == set([('c', )])
assert ask(f[a] > 'a') == set([('c', )])
assert ask(f[a] >= 'c') == set([('c', )])
assert ask(f[a] > 'c') == None
assert ask(f[a] <= 'c') == set([('c', )])
assert ask(f[a] > 'c') == None
assert ask(f[a] in [
'c',
]) == set([('c', )])
assert ask((f[X] == 'c') & (f[Y] == f[X])) == set([('a', 'a')])
assert ask((f[X] == 'c') & (f[Y] == f[X] + '')) == set([('a', 'a')])
assert ask((f[X] == 'c') & (f[Y] == (lambda X: 'c'))) == set([('a',
'a')])
assert ask(f[X] == Y + '') == None
assert ask((Y == 'c') & (f[X] == Y + '')) == set([('c', 'a')])
assert ask((Y == 'c') & (f[X] <= Y + '')) == set([('c', 'a')])
assert ask((Y == 'c') & (f[X] < Y + '')) == None
assert ask((Y == 'c') & (f[X] < 'd' + Y + '')) == set([('c', 'a')])
assert ask((Y == ('a', 'c')) & (f[X] in Y)) == set([(('a', 'c'), 'a')])
assert ask((Y == ('a', 'c')) & (f[X] in (Y + ('z', )))) == set([
(('a', 'c'), 'a')
])
assert ask(f[X] == f[X] + '') == set([('a', )])
@pyDatalog.program()
def function_negation():
assert not (ask(~(f[a] < 'd')))
assert not (ask(~(f[X] < 'd')))
assert ask(~(f[a] in ('d', )))
""" aggregates """
pyDatalog.clear()
@pyDatalog.program()
def sum():
+p(a, c, 1)
+p(b, b, 4)
+p(a, b, 1)
assert (sum(1, 2)) == 3
(a_sum[X] == sum(Y, key=Z)) <= p(X, Z, Y)
assert ask(a_sum[X] == Y) == set([('a', 2), ('b', 4)])
assert ask(a_sum[a] == X) == set([('a', 2)])
assert ask(a_sum[a] == 2) == set([('a', 2)])
assert ask(a_sum[X] == 4) == set([('b', 4)])
assert ask(a_sum[c] == X) == None
assert ask((a_sum[X] == 2) & (p(X, Z, Y))) == set([('a', 'c', 1),
('a', 'b', 1)])
(a_sum2[X] == sum(Y, for_each=X)) <= p(X, Z, Y)
assert ask(a_sum2[a] == X) == set([('a', 1)])
(a_sum3[X] == sum(Y, key=(X, Z))) <= p(X, Z, Y)
assert ask(a_sum3[X] == Y) == set([('a', 2), ('b', 4)])
assert ask(a_sum3[a] == X) == set([('a', 2)])
@pyDatalog.program()
def len():
assert (len((1, 2))) == 2
(a_len[X] == len(Z)) <= p(X, Z, Y)
assert ask(a_len[X] == Y) == set([('a', 2), ('b', 1)])
assert ask(a_len[a] == X) == set([('a', 2)])
assert ask(a_len[X] == 1) == set([('b', 1)])
assert ask(a_len[X] == 5) == None
(a_lenY[X] == len(Y)) <= p(X, Z, Y)
assert ask(a_lenY[a] == X) == set([('a', 1)])
assert ask(a_lenY[c] == X) == None
(a_len2[X, Y] == len(Z)) <= p(X, Y, Z)
assert ask(a_len2[a, b] == X) == set([('a', 'b', 1)])
assert ask(a_len2[a, X] == Y) == set([('a', 'b', 1), ('a', 'c', 1)])
+q(a, c, 1)
+q(a, b, 2)
+q(b, b, 4)
@pyDatalog.program()
def concat():
(a_concat[X] == concat(Y, key=Z, sep='+')) <= q(X, Y, Z)
assert ask(a_concat[X] == Y) == set([('b', 'b'), ('a', 'c+b')])
assert ask(a_concat[a] == 'c+b') == set([('a', 'c+b')])
assert ask(a_concat[a] == X) == set([('a', 'c+b')])
assert ask(a_concat[X] == b) == set([('b', 'b')])
(a_concat2[X] == concat(Y, order_by=(Z, ), sep='+')) <= q(X, Y, Z)
assert ask(a_concat2[a] == X) == set([('a', 'c+b')])
(a_concat3[X] == concat(Y, key=(-Z, ), sep='-')) <= q(X, Y, Z)
assert ask(a_concat3[a] == X) == set([('a', 'b-c')])
@pyDatalog.program()
def min():
assert min(1, 2) == 1
(a_min[X] == min(Y, key=Z)) <= q(X, Y, Z)
assert ask(a_min[X] == Y) == set([('b', 'b'), ('a', 'c')])
assert ask(a_min[a] == 'c') == set([('a', 'c')])
assert ask(a_min[a] == X) == set([('a', 'c')])
assert ask(a_min[X] == 'b') == set([('b', 'b')])
(a_minD[X] == min(Y, order_by=-Z)) <= q(X, Y, Z)
assert ask(a_minD[a] == X) == set([('a', 'b')])
(a_min2[X, Y] == min(Z, key=(X, Y))) <= q(X, Y, Z)
assert ask(a_min2[Y, b] == X) == set([('a', 'b', 2), ('b', 'b', 4)])
assert ask(a_min2[Y, Y] == X) == set([('b', 'b', 4)]), "a_min2"
(a_min3[Y] == min(Z, key=(-X, Z))) <= q(X, Y, Z)
assert ask(a_min3[b] == Y) == set([('b', 4)]), "a_min3"
@pyDatalog.program()
def max():
assert max(1, 2) == 2
(a_max[X] == max(Y, key=-Z)) <= q(X, Y, Z)
assert ask(a_max[a] == X) == set([('a', 'c')])
(a_maxD[X] == max(Y, order_by=Z)) <= q(X, Y, Z)
assert ask(a_maxD[a] == X) == set([('a', 'b')])
@pyDatalog.program()
def rank():
(a_rank1[Z] == rank(for_each=Z, order_by=Z)) <= q(X, Y, Z)
assert ask(a_rank1[X] == Y) == set([(1, 0), (2, 0), (4, 0)])
assert ask(a_rank1[X] == 0) == set([(1, 0), (2, 0), (4, 0)])
assert ask(a_rank1[1] == X) == set([(1, 0)])
assert ask(a_rank1[1] == 0) == set([(1, 0)])
assert ask(a_rank1[1] == 1) == None
# rank
(a_rank[X, Y] == rank(for_each=(X, Y2),
order_by=Z2)) <= q(X, Y, Z) & q(X, Y2, Z2)
assert ask(a_rank[X, Y] == Z) == set([('a', 'b', 1), ('a', 'c', 0),
('b', 'b', 0)])
assert ask(a_rank[a, b] == 1) == set([('a', 'b', 1)])
assert ask(a_rank[a, b] == Y) == set([('a', 'b', 1)])
assert ask(a_rank[a, X] == 0) == set([('a', 'c', 0)])
assert ask(a_rank[a, X] == Y) == set([('a', 'b', 1), ('a', 'c', 0)])
assert ask(a_rank[X, Y] == 1) == set([('a', 'b', 1)])
assert ask(a_rank[a, y] == Y) == None
# reversed
(b_rank[X, Y] == rank(for_each=(X, Y2),
order_by=-Z2)) <= q(X, Y, Z) & q(X, Y2, Z2)
assert ask(b_rank[X, Y] == Z) == set([('a', 'b', 0), ('a', 'c', 1),
('b', 'b', 0)])
assert ask(b_rank[a, b] == 0) == set([('a', 'b', 0)])
assert ask(b_rank[a, b] == Y) == set([('a', 'b', 0)])
assert ask(b_rank[a, X] == 1) == set([('a', 'c', 1)])
assert ask(b_rank[a, X] == Y) == set([('a', 'b', 0), ('a', 'c', 1)])
assert ask(b_rank[X, Y] == 0) == set([('a', 'b', 0), ('b', 'b', 0)])
assert ask(b_rank[a, y] == Y) == None
@pyDatalog.program()
def running_sum():
# running_sum
(a_run_sum[X, Y] == running_sum(
Z2, for_each=(X, Y2), order_by=Z2)) <= q(X, Y, Z) & q(X, Y2, Z2)
assert ask(a_run_sum[X, Y] == Z) == set([('a', 'b', 3), ('a', 'c', 1),
('b', 'b', 4)])
assert ask(a_run_sum[a, b] == 3) == set([('a', 'b', 3)])
assert ask(a_run_sum[a, b] == Y) == set([('a', 'b', 3)])
assert ask(a_run_sum[a, X] == 1) == set([('a', 'c', 1)])
assert ask(a_run_sum[a, X] == Y) == set([('a', 'b', 3), ('a', 'c', 1)])
assert ask(a_run_sum[X, Y] == 4) == set([('b', 'b', 4)])
assert ask(a_run_sum[a, y] == Y) == None
(b_run_sum[X, Y] == running_sum(
Z2, for_each=(X, Y2), order_by=-Z2)) <= q(X, Y, Z) & q(X, Y2, Z2)
assert ask(b_run_sum[X, Y] == Z) == set([('a', 'b', 2), ('a', 'c', 3),
('b', 'b', 4)])
assert ask(b_run_sum[a, b] == 2) == set([('a', 'b', 2)])
assert ask(b_run_sum[a, b] == Y) == set([('a', 'b', 2)])
assert ask(b_run_sum[a, X] == 3) == set([('a', 'c', 3)])
assert ask(b_run_sum[a, X] == Y) == set([('a', 'b', 2), ('a', 'c', 3)])
assert ask(b_run_sum[X, Y] == 4) == set([('b', 'b', 4)])
assert ask(b_run_sum[a, y] == Y) == None
""" simple in-line queries """
X = pyDatalog.Variable()
assert ((X == 1) >= X) == 1
assert ((X == 1) & (X != 2) >= X) == 1
assert set(X._in((1, 2))) == set([(1, ), (2, )])
assert ((X == 1) & (X._in((1, 2)))) == [(1, )]
""" interface with python classes """
class A(pyDatalog.Mixin):
def __init__(self, b):
super(A, self).__init__()
self.b = b
def __repr__(self):
return self.b
@pyDatalog.program(
) # indicates that the following method contains pyDatalog clauses
def _():
(A.c[X] == N) <= (A.b[X] == N)
(A.len[X] == len(N)) <= (A.b[X] == N)
@classmethod
def _pyD_x1(cls, X):
if X.is_const() and X.id.b == 'za':
yield (X.id, )
else:
for X in pyDatalog.metaMixin.__refs__[cls]:
if X.b == 'za':
yield (X, )
a = A('a')
b = A('b')
assert a.c == 'a'
X, Y = pyDatalog.variables(2)
assert (A.c[X] == 'a') == [(a, )]
assert (A.c[X] == 'a')[0] == (a, )
assert list(X.data) == [a]
assert X.v() == a
assert ((A.c[a] == X) >= X) == 'a'
assert ((A.c[a] == X) & (A.c[a] == X) >= X) == 'a'
assert ((A.c[a] == X) & (A.c[b] == X) >= X) == None
(A.c[X] == 'b') & (A.b[X] == 'a')
assert list(X.data) == []
(A.c[X] == 'a') & (A.b[X] == 'a')
assert list(X.data) == [a]
result = (A.c[X] == 'a') & (A.b[X] == 'a')
assert result == [(a, )]
assert (A.c[a] == 'a') == [()]
assert (A.b[a] == 'a') == [()]
assert (A.c[a] == 'a') & (A.b[a] == 'a') == [()]
assert (A.b[a] == 'f') == []
assert ((A.c[a] == 'a') & (A.b[a] == 'f')) == []
""" filters on python classes """
assert (A.b[X] != Y) == [(a, None), (b, None)]
assert (A.b[X] != 'a') == [(b, )]
assert (A.b[X] != 'z') == [(a, ), (b, )]
assert (A.b[a] != 'a') == []
assert list(A.b[b] != 'a') == [()]
assert ((A.b[b] != 'a') & (A.b[b] != 'z')) == [()]
assert (A.b[X] < Y) == [(a, None), (b, None)]
assert (A.b[X] < 'a') == []
assert (A.b[X] < 'z') == [(a, ), (b, )]
assert (A.b[a] < 'b') == [()]
assert (A.b[b] < 'a') == []
assert ((A.b[b] < 'z') & (A.b[b] != 'z')) == [()]
assert (A.b[X] <= 'a') == [(a, )]
assert (A.b[X] <= 'z') == [(a, ), (b, )]
assert (A.b[a] <= 'b') == [()]
assert (A.b[b] <= 'a') == []
assert ((A.b[b] <= 'z') & (A.b[b] != 'z')) == [()]
assert (A.b[X] > 'a') == [(b, )]
assert (A.b[X] >= 'a') == [(a, ), (b, )]
assert (A.c[X] <= 'a') == [(a, )]
assert (A.c[X] <= 'a' + '') == [(a, )]
assert (A.c[X]._in(('a', ))) == [(a, )]
assert (A.c[X]._in(('a', ) + ('z', ))) == [(a, )]
assert ((Y == ('a', )) & (A.c[X]._in(Y))) == [(('a', ), a)
] # TODO make ' in ' work
assert ((Y == ('a', )) & (A.c[X]._in(Y + ('z', )))) == [
(('a', ), a)
] # TODO make ' in ' work
assert (A.c[X]._in(('z', ))) == []
# more complex queries
assert ((Y == 'a') & (A.b[X] != Y)) == [
('a', b)
] # order of appearance of the variables !
assert (A.len[X] == Y) == [(b, 1), (a, 1)]
assert (A.len[a] == Y) == [(1, )]
""" subclass """
class Z(A):
def __init__(self, z):
super(Z, self).__init__(z + 'a')
self.z = z
def __repr__(self):
return self.z
@pyDatalog.program(
) # indicates that the following method contains pyDatalog clauses
def _():
(Z.w[X] == N) <= (Z.z[X] != N)
@classmethod
def _pyD_query(cls, pred_name, args):
if pred_name == 'Z.pred':
if args[0].is_const() and args[0].id.b != 'za':
yield (args[0].id, )
else:
for X in pyDatalog.metaMixin.__refs__[cls]:
if X.b != 'za':
yield (X, )
else:
raise AttributeError
z = Z('z')
assert z.z == 'z'
assert (Z.z[X] == 'z') == [(z, )]
assert ((Z.z[X] == 'z') & (Z.z[X] > 'a')) == [(z, )]
assert list(X.data) == [z]
try:
a.z == 'z'
except Exception as e:
e_message = e.message if hasattr(e, 'message') else e.args[0]
if e_message != "Predicate without definition (or error in resolver): A.z[1]==/2":
print(e_message)
else:
assert False
try:
(Z.z[a] == 'z') == None
except Exception as e:
e_message = e.message if hasattr(e, 'message') else e.args[0]
if e_message != "Object is incompatible with the class that is queried.":
print(e_message)
else:
assert False
assert (Z.b[X] == Y) == [(z, 'za')]
assert (Z.c[X] == Y) == [(z, 'za')]
assert ((Z.c[X] == Y) & (Z.c[X] > 'a')) == [(z, 'za')]
assert (Z.c[X] > 'a') == [(z, )]
assert ((Z.c[X] > 'a') & (A.c[X] == 'za')) == [(z, )]
assert (A.c[X] == 'za') == [(z, )]
assert (A.c[z] == 'za') == [()]
assert (z.b) == 'za'
assert (z.c) == 'za'
w = Z('w')
w = Z('w') # duplicated to test __refs__[cls]
assert (Z.x(X)) == [(z, )]
assert not (~Z.x(z))
assert ~Z.x(w)
assert ~(Z.z[w] == 'z')
assert (Z.pred(X)) == [(w, )] # not duplicated !
assert (Z.pred(X) & ~(Z.z[X] >= 'z')) == [(w, )]
assert (Z.x(X) & ~(Z.pred(X))) == [(z, )]
assert (Z.len[X] == Y) == [(w, 1), (z, 1)]
assert (Z.len[z] == Y) == [(1, )]
# TODO print (A.b[w]==Y)
""" python resolvers """
@pyDatalog.predicate()
def p(X, Y):
yield (1, 2)
yield (2, 3)
assert pyDatalog.ask('p(X,Y)') == set([(1, 2), (2, 3)])
assert pyDatalog.ask('p(1,Y)') == set([(1, 2)])
assert pyDatalog.ask('p(1,2)') == set([(1, 2)])
""" error detection """
@pyDatalog.program()
def _():
pass
error = False
try:
_()
except:
error = True
assert error
def assert_error(code, message='^$'):
_error = False
try:
pyDatalog.load(code)
except Exception as e:
e_message = e.message if hasattr(
e, 'message') else e.args[0] # python 2 and 3
if not re.match(message, e_message):
print(e_message)
_error = True
assert _error
def assert_ask(code, message='^$'):
_error = False
try:
pyDatalog.ask(code)
except Exception as e:
e_message = e.message if hasattr(e, 'message') else e.args[0]
if not re.match(message, e_message):
print(e_message)
_error = True
assert _error
assert_error('ask(z(a),True)', 'Too many arguments for ask \!')
assert_error('ask(z(a))',
'Predicate without definition \(or error in resolver\): z/1')
assert_error(
"+ farmer(farmer(moshe))",
"Syntax error: Literals cannot have a literal as argument : farmer\[\]"
)
assert_error(
"+ manager[Mary]==John",
"Left-hand side of equality must be a symbol or function, not an expression."
)
assert_error(
"manager[X]==Y <= (X==Y)",
"Syntax error: please verify parenthesis around \(in\)equalities")
assert_error("p(X) <= (Y==2)", "Can't create clause")
assert_error(
"p(X) <= X==1 & X==2",
"Syntax error: please verify parenthesis around \(in\)equalities")
assert_error("p(X) <= (manager[X]== min(X))",
"Error: argument missing in aggregate")
assert_error("p(X) <= (manager[X]== max(X, order_by=X))",
"Aggregation cannot appear in the body of a clause")
assert_error("q(min(X, order_by=X)) <= p(X)",
"Syntax error: Incorrect use of aggregation\.")
assert_error(
"manager[X]== min(X, order_by=X) <= manager(X)",
"Syntax error: please verify parenthesis around \(in\)equalities")
assert_error(
"(manager[X]== min(X, order_by=X+2)) <= manager(X)",
"order_by argument of aggregate must be variable\(s\), not expression\(s\)."
)
assert_error("ask(X<1)",
'Error: left hand side of comparison must be bound: =X<1/1')
assert_error("ask(X<Y)",
'Error: left hand side of comparison must be bound: =X<Y/2')
assert_error("ask(1<Y)",
'Error: left hand side of comparison must be bound: =Y>1/1')
assert_error(
"ask( (A.c[X]==Y) & (Z.c[X]==Y))",
"TypeError: First argument of Z.c\[1\]==\('.','.'\) must be a Z, not a A "
)
assert_ask(
"A.u[X]==Y",
"Predicate without definition \(or error in resolver\): A.u\[1\]==/2")
assert_ask(
"A.u[X,Y]==Z",
"Predicate without definition \(or error in resolver\): A.u\[2\]==/3")
assert_error('(a_sum[X] == sum(Y, key=Y)) <= p(X, Z, Y)',
"Error: Duplicate definition of aggregate function.")
assert_error(
'(two(X)==Z) <= (Z==X+(lambda X: X))',
'Syntax error near equality: consider using brackets. two\(X\)')
assert_error('p(X) <= sum(X, key=X)', 'Invalid body for clause')
assert_error(
'ask(- manager[X]==1)',
"Left-hand side of equality must be a symbol or function, not an expression."
)
assert_error("p(X) <= (X=={})", "unhashable type: 'dict'")
""" SQL Alchemy """
from sqlalchemy import create_engine
from sqlalchemy import Column, Integer, String, ForeignKey
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker, relationship
engine = create_engine('sqlite:///:memory:',
echo=False) # create database in memory
Session = sessionmaker(bind=engine)
session = Session()
Base = declarative_base(cls=pyDatalog.Mixin,
metaclass=pyDatalog.sqlMetaMixin)
Base.session = session
class Employee(Base): # --> Employee inherits from the Base class
__tablename__ = 'employee'
name = Column(String, primary_key=True)
manager_name = Column(String, ForeignKey('employee.name'))
salary = Column(Integer)
def __init__(self, name, manager_name, salary):
super(Employee, self).__init__()
self.name = name
self.manager_name = manager_name # direct manager of the employee, or None
self.salary = salary # monthly salary of the employee
def __repr__(self): # specifies how to display the employee
return "Employee: %s" % self.name
@pyDatalog.program(
) # --> the following function contains pyDatalog clauses
def Employee():
(Employee.manager[X]
== Y) <= (Employee.manager_name[X] == Z) & (Z == Employee.name[Y])
# the salary class of employee X is computed as a function of his/her salary
# this statement is a logic equality, not an assignment !
Employee.salary_class[X] = Employee.salary[X] // 1000
# all the indirect managers of employee X are derived from his manager, recursively
Employee.indirect_manager(
X, Y) <= (Employee.manager[X] == Y) & (Y != None)
Employee.indirect_manager(
X, Y) <= (Employee.manager[X]
== Z) & Employee.indirect_manager(Z, Y) & (Y != None)
# count the number of reports of X
(Employee.report_count[X] == len(Y)) <= Employee.indirect_manager(
Y, X)
Employee.p(X, Y) <= (Y <= Employee.salary[X] + 1)
Base.metadata.create_all(engine)
John = Employee('John', None, 6800)
Mary = Employee('Mary', 'John', 6300)
Sam = Employee('Sam', 'Mary', 5900)
session.add(John)
session.add(Mary)
session.add(Sam)
session.commit()
assert (John.salary_class == 6)
X = pyDatalog.Variable()
result = (Employee.salary[X] == 6300
) # notice the similarity to a pyDatalog query
assert result == [
(Mary, ),
]
assert (X._value() == [
Mary,
]) # prints [Employee: Mary]
assert (X.v() == Mary) # prints Employee:Mary
result = (Employee.indirect_manager(Mary, X))
assert result == [
(John, ),
]
assert (X.v() == John) # prints [Employee: John]
Mary.salary_class = ((Employee.salary_class[Mary] == X) >= X)
Mary.salary = 10000
assert Mary.salary_class != ((Employee.salary_class[Mary] == X) >= X)
X, Y, N = pyDatalog.variables(3)
result = (Employee.salary[X] == 6800) & (Employee.name[X] == N)
assert result == [
(John, 'John'),
]
assert N.v() == 'John'
result = (Employee.salary[X] == Employee.salary[X])
assert result == [(John, ), (Mary, ), (Sam, )]
result = (Employee.p(X, 1))
assert result == [(John, ), (Mary, ), (Sam, )]
result = (Employee.salary[X] < Employee.salary[X] + 1)
assert result == [(John, ), (Mary, ), (Sam, )]
result = (Employee.salary[John] == N) & Employee.p(John, N)
assert result == [(6800, )]
result = (Employee.salary[X] == 6800) & (Employee.salary[X]
== N) & Employee.p(X, N)
assert result == [(John, 6800)]
"""
def tryTacticOptions(maxTactics,debug=False):
#Initialize utilities dictionary for each of the tactic options
utilities = {}
#if debug:
#bestUtility = float(0)
originalUtil = float(determineResidualUtility())
bestUtility = originalUtil
print("Original utility: " + str(originalUtil))
utilities[""] = originalUtil
#For progress tracking
numOptions = 0
for numTactics in range(1,maxTactics+1):
numOptions += len(list(itertools.combinations(tacticsIter(),numTactics)))
#TODO? Put following line back in
#utilities[None] = float(determineResidualUtility())
setNumber = 0
print("Tactic options: " + str(numOptions))
#TODO Extra double-check to ensure logic is correct here...should be all possible vulnerabilities
#TODO Also try zero tactics! Maybe current config is the best
for numTactics in range(1,maxTactics+1):
#Creator iterator of tactic sets to try
tacticOptions = itertools.combinations(tacticsIter(),numTactics)
#if numTactics == 0:
# tacticOptions = iter([[]])
for tacticSet in tacticOptions:
#Print status
setNumber += 1
percentComplete = 100 * setNumber / numOptions
print(str(percentComplete) + "%...\r"),
sys.stdout.flush()
#print "***************************************"
#print "Tactic Set:"
#print tacticSet
#Apply the tactics in the selected tactic set
for tactic in tacticSet:
#print tactic
args = tactic[1:]
if tactic[0] == "retract":
pyDatalog.retract_fact(*args)
if bidirectional:
if tactic[1] == "networkConnectsTo": #retract the symmetric connection
#TODO Did we already do this in the code above? Is it a safe assumption that the reversed connection has the same CIA attributes?
#Changed
argsRev = [args[0],args[2],args[1],args[3],args[4],args[5]]
pyDatalog.retract_fact(*argsRev)
else:
pyDatalog.assert_fact(*args)
#Determine utility and put in dictionary
#This weighted value includes cost of tactic: A cost of 1 per tactic
tsStr = str(sorted(list(tacticSet))) #key
residualUtil = float(determineResidualUtility() - numTactics) #value
if debug and residualUtil>=bestUtility:
print("New optimal: " + str(residualUtil) + str(tacticSet))
bestUtility = residualUtil
#TODO The check for multiple tactics may not be necessary in some versions of this code
if tsStr in utilities:
#print("MULTIPLE: " + tsStr + ": " + str(residualUtil))
if residualUtil < utilities[tsStr]:
utilities[tsStr] = residualUtil
else:
utilities[tsStr] = residualUtil
#Undo the tactic application
for tactic in tacticSet:
args = tactic[1:]
if tactic[0] == "assert":
pyDatalog.retract_fact(*args)
#Changed
#TODO Is this a safe assumption that the reversed connection has the same CIA attributes?
if (tactic[1] == "networkConnectsToWithAttributes") and (bidirectional == True):
pyDatalog.retract_fact(args[0],args[2],args[1],args[3],args[4],args[5])
else:
pyDatalog.assert_fact(*args)
#pprint.pprint(utilities)
#Find the best performing tactic set in the dictionary created earlier
bestOptions(utilities)
def forgetData(self, estereotipo, nombre):
pyDatalog.retract_fact(estereotipo, nombre)