def buscar():
print(
'Opciones para buscar personas\n1: Buscar a todos\n2: Buscar por nombre\n3: Volver\n'
)
sel = None
while sel not in ("1", "2", "3"):
sel = input('Seleccione una opción:\n')
if (sel == '1'):
print(pyDatalog.ask("is_person(X)"))
elif (sel == '2'):
nombre = input("Escribe un nombre para consultar:\n")
W = pyDatalog.ask("is_person('" + nombre + "')")
if (str(W) == 'None'):
op = None
while op not in ("s", "n", "S", "N"):
op = input(
"No existe esta persona, ¿Desea agregarla?(S/N)")
if (op == 's' or op == 'S'):
agregar(nombre)
elif (op == "n" or op == "N"):
break
else:
print('Solo puede agregar S o N')
else:
print(W.answers)
elif (sel == '3'):
break
else:
print("Ingrese una opción valida")
def assertPatient(self, name, age, sick):
Logic(self.first)
#metodo que agrega un paciente a la base de conocimiento
pyDatalog.assert_fact('age', name, age)
pyDatalog.assert_fact('sick_of', name, sick)
tq2 = "sick_of(" + name + ",Y)"
tq3 = "require_limits_temperature(" + name + ",A,B)"
tq4 = "require_temperature(" + name + ",A)"
tq5 = "is_in_stage(" + name + ",A)"
q2 = pyDatalog.ask(tq2)
q3 = pyDatalog.ask(tq3)
q4 = pyDatalog.ask(tq4)
q5 = pyDatalog.ask(tq5)
db = self.con.conexion()
coleccion = db['sensor_temp']
query = {"activo": "t"}
data = {
"$set": {
"act0_status": "f",
"act1_status": "f",
"enfermedad": q2.answers[0][0],
"temp_min": int(q3.answers[0][1]),
"temp_max": int(q3.answers[0][0]),
"temp_pref": int(q4.answers[0][0]),
"stage": q5.answers[0][0]
}
}
coleccion.update_one(query, data)
print("mongodb updated")
return 'saved'
def addRecipe(name, ingredients, instructions):
isAlreadyDefined = pyDatalog.ask("hasID('" + name + "', 'recipe', Z)")
if (not isAlreadyDefined == None):
print("This recipe is already defined")
return isAlreadyDefined.answers[0][0]
# insert recipe in database
database = connectToDatabase()
mydb = database[0]
mycursor = database[1]
sqlRezepte = "INSERT INTO `Rezepte` (`Name`, `Anleitung`) VALUES (%s, %s)"
valRezepte = [(name, instructions)]
mycursor.executemany(sqlRezepte, valRezepte)
mydb.commit()
recipeNumber = mycursor.lastrowid
id = "recipe" + str(recipeNumber)
# add recipe to datalog
pyDatalog.assert_fact('hasID', name, "recipe", id)
pyDatalog.assert_fact('recipeInstructions', name, instructions)
# insert ingredients in database
for i in ingredients:
if isValidId(i[0]):
if "ingredient" in i[0]:
sqlEnthaeltLebensmittel = "INSERT INTO `enthaeltLebensmittel` (`RezeptId`, `LebensmittelId`, `Menge`) VALUES (%s, %s, %s)"
valEnthaeltLebensmittel = [(recipeNumber, int(i[0][10:11]),
i[1])]
mycursor.executemany(sqlEnthaeltLebensmittel,
valEnthaeltLebensmittel)
mydb.commit()
if "recipe" in i[0]:
sqlEnthaeltrezept = "INSERT INTO `enthaeltRezept` (`RezeptId`, `KomponentenRezeptId`, `Menge`) VALUES (%s, %s, %s)"
valEnthaeltrezept = [(recipeNumber, int(i[0][6:7]), i[1])]
mycursor.executemany(sqlEnthaeltrezept, valEnthaeltrezept)
mydb.commit()
# add ingredient to datalog
ingredient = pyDatalog.ask(("hasID(X, Y, '" + i[0] + "')"))
pyDatalog.assert_fact('containsIngredient', name,
ingredient.answers[0][0], i[1])
else:
print(i[0] + " is no valid id")
# add weight to datalog
ingredients = pyDatalog.ask("containsIngredient('" + name + "', Y, Z)")
weight = 0
if not ingredients == None:
for i in ingredients.answers:
weight += i[1]
pyDatalog.assert_fact('weightPerServing', name, weight)
pyDatalog.assert_fact('hasCaloriesPer100g', name, getCaloriesPer100g(name))
if mydb.is_connected():
mycursor.close()
mydb.close()
return id
def getRecipeDetails(recipeID):
recipeInstructions = pyDatalog.ask("hasID(X, 'recipe', '" + recipeID +
"') & recipeInstructions(X, Y)")
recipeIngredients = pyDatalog.ask("containsIngredient('" +
recipeInstructions.answers[0][0] +
"', Y, Z)")
recipeIngredientsReturnValue = []
if not recipeIngredients == None:
recipeIngredientsReturnValue = recipeIngredients.answers
return recipeInstructions.answers + recipeIngredientsReturnValue
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
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
def test():
query = "parent(X, Y)"
answers = pyDatalog.ask(query).answers
for ans in answers:
print(ans)
print()
print()
query = 'decendent(X, "Fernanda del Carpio")'
answers = sorted(pyDatalog.ask(query).answers)
expected_answers = ("Aureliano (II)", )
print(answers)
pass
def getCaloriesPer100g(recipeName):
calorieSum = 0
calories = pyDatalog.ask("baseIngredientWithCalories('" + recipeName +
"', X, Y)")
if not calories == None:
for c in calories.answers:
calorieSum += c[1]
return calorieSum * 100 / pyDatalog.ask("weightPerServing('" +
recipeName +
"', X)").answers[0][0]
else:
return 0
def readPatient(self, name):
Logic(self.first)
#metodo que consulta los datos del paciente actualmente presente en la base de conocimiento
returnvalue = ""
try:
tq1 = "age(" + name + ",Y)"
tq2 = "sick_of(" + name + ",Y)"
q1 = pyDatalog.ask(tq1)
q2 = pyDatalog.ask(tq2)
returnvalue = "{'name':'" + name + "','age':'" + q1.answers[0][
0] + "','sick':'" + q2.answers[0][0] + "'}"
except:
returnvalue = "NOPATIENT"
return returnvalue
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 addIngredient(name, calorie, glutenFree, lactoseFree, vegan, vegetarian):
isAlreadyDefined = pyDatalog.ask("hasID('" + name + "', 'ingredient', Z)")
if (not isAlreadyDefined == None):
print("This ingredient is already defined")
return isAlreadyDefined.answers[0][0]
# insert ingredient in database
database = connectToDatabase()
mydb = database[0]
mycursor = database[1]
sqlZutat = "INSERT INTO `Lebensmittel` (`Name`, `KalorienPro100g`, `Fleisch`, `Tierprodukt`, `Gluten`, `Krebstiere`, `Eier`, `Fisch`, `Erdnuesse`, `Sojabohnen`, `Milch`, `Schalenfruechte`, `Sellerie`, `Sesamsamen`, `Schwefeldioxid`, `Lupinien`, `Weichtiere`) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)"
valZutat = [(name, calorie, 0 if vegetarian else 1, 0 if vegan else 1,
0 if glutenFree else 1, 0, 0, 0, 0, 0,
0 if lactoseFree else 1, 0, 0, 0, 0, 0, 0)]
mycursor.executemany(sqlZutat, valZutat)
mydb.commit()
ingredientNumber = mycursor.lastrowid
id = "ingredient" + str(ingredientNumber)
# add ingredient to datalog
pyDatalog.assert_fact('hasID', name, "ingredient", id)
pyDatalog.assert_fact('hasCaloriesPer100g', name, calorie)
if not glutenFree:
pyDatalog.assert_fact('containsGluten', name)
if not lactoseFree:
pyDatalog.assert_fact('containsLactose', name)
if not vegetarian:
pyDatalog.assert_fact('containsMeat', name)
if (not vegetarian) | (not vegan):
pyDatalog.assert_fact('containsAnimalProduct', name)
pyDatalog.assert_fact('weightPerServing', name, 1)
def queen(thread_name):
n = int(random.random() * 8) + 1 # 1 to 8
Logic()
queens(X0) <= (X0._in(range(n)))
queens(X0,X1) <= queens(X0) & next_queen(X0,X1)
queens(X0,X1,X2) <= queens(X0,X1) & next_queen(X0,X1,X2)
queens(X0,X1,X2,X3) <= queens(X0,X1,X2) & next_queen(X0,X1,X2,X3)
queens(X0,X1,X2,X3,X4) <= queens(X0,X1,X2,X3) & next_queen(X0,X1,X2,X3,X4)
queens(X0,X1,X2,X3,X4,X5) <= queens(X0,X1,X2,X3,X4) & next_queen(X0,X1,X2,X3,X4,X5)
queens(X0,X1,X2,X3,X4,X5,X6) <= queens(X0,X1,X2,X3,X4,X5) & next_queen(X0,X1,X2,X3,X4,X5,X6)
queens(X0,X1,X2,X3,X4,X5,X6,X7) <= queens(X0,X1,X2,X3,X4,X5,X6) & next_queen(X0,X1,X2,X3,X4,X5,X6,X7)
next_queen(X0,X1) <= queens(X1) & ok(X0,1,X1)
next_queen(X0,X1,X2) <= next_queen(X1,X2) & ok(X0,2,X2)
next_queen(X0,X1,X2,X3) <= next_queen(X1,X2,X3) & ok(X0,3,X3)
next_queen(X0,X1,X2,X3,X4) <= next_queen(X1,X2,X3,X4) & ok(X0,4,X4)
next_queen(X0,X1,X2,X3,X4,X5) <= next_queen(X1,X2,X3,X4,X5) & ok(X0,5,X5)
next_queen(X0,X1,X2,X3,X4,X5,X6) <= next_queen(X1,X2,X3,X4,X5,X6) & ok(X0,6,X6)
next_queen(X0,X1,X2,X3,X4,X5,X6,X7) <= next_queen(X1,X2,X3,X4,X5,X6,X7) & ok(X0,7,X7)
query = pyDatalog.ask("queens(%s)" % (",".join("X%s" % i for i in range(n))))
answers = query.answers if query else []
result = "OK" if len(answers) == [1,0,0,2,10,4,40,92][n-1] else "* not OK ! *"
print("%s : n = %d %s " % (thread_name, n, result))
def leer_perfil(self, datos):
str_experto = datos[0]
str_medico = ""
if(datos[1] == "si" and datos[2]=="si" and datos[3] == "si"):
str_medico = "si"
elif(datos[1] == "si" and datos[2]=="no" and datos[3] == "si"):
str_medico = "si"
else:
str_medico = "no"
Logic(self.conocimiento)
try:
pyDatalog.assert_fact("resp_experto",str_experto)
pyDatalog.assert_fact("resp_medico",str_medico)
q = "tipo_usuario(X,Y)"
rq_3 = pyDatalog.ask(q)
arr_resp = [
rq_3.answers[0][0],
rq_3.answers[0][1]
]
return arr_resp
except:
print("error")
def __listarPalabrasComunesIdiomas(_idiomaA, _idiomaB):
# Listar todas las palabras comunes entre dos idiomas
consulta = ask('palabrasComunes(' + _idiomaA + ', ' + _idiomaB + ', R1)')
if (consulta):
return consulta.answers
else:
return "No hubo coincidencia."
def queen(thread_name):
n = int(random.random() * 8) + 1 # 1 to 8
Logic()
queens(X0) <= (X0._in(range(n)))
queens(X0,X1) <= queens(X0) & next_queen(X0,X1)
queens(X0,X1,X2) <= queens(X0,X1) & next_queen(X0,X1,X2)
queens(X0,X1,X2,X3) <= queens(X0,X1,X2) & next_queen(X0,X1,X2,X3)
queens(X0,X1,X2,X3,X4) <= queens(X0,X1,X2,X3) & next_queen(X0,X1,X2,X3,X4)
queens(X0,X1,X2,X3,X4,X5) <= queens(X0,X1,X2,X3,X4) & next_queen(X0,X1,X2,X3,X4,X5)
queens(X0,X1,X2,X3,X4,X5,X6) <= queens(X0,X1,X2,X3,X4,X5) & next_queen(X0,X1,X2,X3,X4,X5,X6)
queens(X0,X1,X2,X3,X4,X5,X6,X7) <= queens(X0,X1,X2,X3,X4,X5,X6) & next_queen(X0,X1,X2,X3,X4,X5,X6,X7)
next_queen(X0,X1) <= queens(X1) & ok(X0,1,X1)
next_queen(X0,X1,X2) <= next_queen(X1,X2) & ok(X0,2,X2)
next_queen(X0,X1,X2,X3) <= next_queen(X1,X2,X3) & ok(X0,3,X3)
next_queen(X0,X1,X2,X3,X4) <= next_queen(X1,X2,X3,X4) & ok(X0,4,X4)
next_queen(X0,X1,X2,X3,X4,X5) <= next_queen(X1,X2,X3,X4,X5) & ok(X0,5,X5)
next_queen(X0,X1,X2,X3,X4,X5,X6) <= next_queen(X1,X2,X3,X4,X5,X6) & ok(X0,6,X6)
next_queen(X0,X1,X2,X3,X4,X5,X6,X7) <= next_queen(X1,X2,X3,X4,X5,X6,X7) & ok(X0,7,X7)
query = pyDatalog.ask("queens(%s)" % (",".join("X%s" % i for i in range(n))))
answers = query.answers if query else []
result = "OK" if len(answers) == [1,0,0,2,10,4,40,92][n-1] else "* not OK ! *"
print("%s : n = %d %s " % (thread_name, n, result))
def answerQuestion(peer_name, net, user_password):
policy_filename = 'entities/' + peer_name + '/datalog_policy.data'
data_filename = 'entities/' + peer_name + '/data.data'
queryString = net.recv();
queryList = queryString.split(network_protocol.SEPARATOR)
print(queryList)
trusted_peer_dict = getTrustedPeers(peer_name)
for other_peer, trusted_with in trusted_peer_dict.items():
data = getDataFromTrustedPeer(peer_name, other_peer, user_password)
pruned_data = prune(data, trusted_with)
add_facts(pruned_data)
# Add our own data
if(fileIO.fileExists(data_filename)):
add_facts(fileIO.readFile(data_filename))
pyDatalog.load(fileIO.readFile(policy_filename))
print("Asking question: " + queryList[0])
query_result = pyDatalog.ask(queryList[0]);
print('Query result:',query_result)
if(query_result != None):
net.send(queryList[1])
else:
net.send(queryList[2])
def __palabrasEnUnIdiomaOriginadasPorPalabra(_palabra, _idioma):
#Obtener el conjunto de todas las palabras en un idioma originadas por una palabra específica
consulta = ask('hijosIdioma(' + _palabra + ',' + _idioma + ', R)')
if (consulta):
return consulta.answers
else:
return "No se obtuvo coincidencias"
def __idiomasRelacionadosPalabra(_palabra):
# Listar los idiomas relacionados con una palabra
consulta = ask('_soloIdiomas(' + _palabra + ', R)')
if (consulta):
return consulta.answers
else:
return "No hay idiomas relacionados"
def __numeroPalabrasComunesIdiomas(_idiomaA, _idiomaB):
# Contar todas las palabras comunes entre dos idiomas
consulta = ask('contarPalabrasComunes('+_idiomaA+', '+_idiomaB+', R1)')
if (consulta):
return consulta.answers[0][0][0]
else:
return 0
def _(): # the function name is ignored
pyDatalog.load(mc)
#pyDatalog.load("""
#+ (factorial[1]==1)
#(factorial[N] == F) <= (N > 1) & (F == N*factorial[N-1])
#""")
print(pyDatalog.ask('factorial[4]==F'))
def materialize(facts, clauses, parser):
logger.info('Asserting facts ..')
for f in facts:
# Each fact is asserted using the index of the subject, predicate and object for avoiding syntax issues
s_idx = parser.entity_to_index[f.argument_names[0]]
p_idx = parser.predicate_to_index[f.predicate_name]
o_idx = parser.entity_to_index[f.argument_names[1]]
# Asserting p(S, P, O)
pyDatalog.assert_fact('p', s_idx, p_idx, o_idx)
rules_str = '\n'.join(
[clause_to_str(clause, parser) for clause in clauses])
pyDatalog.load(rules_str)
# Asking for all P(s, p, o) triples which hold true in the Knowledge Graph
logger.info('Querying triples ..')
_ans = pyDatalog.ask('p(S, P, O)')
index_to_predicate = {
idx: p
for p, idx in parser.predicate_to_index.items()
}
index_to_entity = {idx: e for e, idx in parser.entity_to_index.items()}
# Generating a list of inferred facts by replacing each entity and predicate index with their corresponding symbols
inferred_facts = [
Fact(index_to_predicate[p], [index_to_entity[s], index_to_entity[o]])
for (s, p, o) in sorted(_ans.answers)
]
return inferred_facts
def __esHija(_idiomaHijo, _hijo, _idiomaPadre, _padre):
# Determinar si una palabra es hij@ de otra
if (ask('esHijo(' + _idiomaHijo + ',' + _hijo + ',' + _idiomaPadre + ',' +
_padre + ', R)')):
return "Sí es hija"
else:
return "No es hija"
def __sonPrimas(_idiomaA, _nombreA, _idiomaB, _nombreB):
# Determinar si dos palabras son prim@s
if (ask('sonPrimas(' + _idiomaA + ',' + _nombreA + ',' + _idiomaB + ',' +
_nombreB + ')')):
return "Son primas"
else:
return "No son primas"
def connectionsAnswer():
connectionsAnswer = pyDatalog.ask('networkConnectsTo(ServiceA,ServiceB,C*K,IOK,AOK)')
if connectionsAnswer == None:
connections = []
else:
connections = connectionsAnswer.answers
return connections
def check_logic(thread_name, n):
query = pyDatalog.ask("queens(%s)" % (",".join("X%s" % i
for i in range(n))))
answers = query.answers if query else []
result = "OK" if len(answers) == [1, 0, 0, 2, 10, 4, 40, 92
][n - 1] else answers
print("%s : n = %d %s " % (thread_name, n, result))
def __esTia(_idiomaTio, _tio, _idiomaSobrino, _sobrino):
# Determinar si una palabra es tí@
if (ask('esTio(' + _idiomaTio + ',' + _tio + ',' + _idiomaSobrino + ',' +
_sobrino + ', R)')):
return "Sí es tía"
else:
return "No es tía"
def __listarIdiomasAportaronOtro(_idioma):
# LIstar todos los idiomas que aportaron a otro. Similar al anterior pero debe incluir porcentaje para todos los idiomas
consulta = ask('contribucionXidioma(' + _idioma + ', R1, R2)')
if (consulta):
return consulta.answers
else:
return "Ninguno"
def __gradoPrimas(_nombreA, _nombreB):
# Determina si son primas y en qué grado
consulta = ask('gradoPrimos(' + _nombreA + ',' + _nombreB + ', R)')
if (consulta):
return "Son primas", consulta.answers[0][0]
else:
return "No son primas", 0
def __idiomaMasAporto(_idioma):
# Idioma que más aportó a otro (e.g. latín a español). Medir basado en porcentaje
consulta = ask('__mayorContribucion(' + _idioma + ', R1)')
if (consulta):
return consulta.answers[0][0]
else:
return "Ninguno"
def designOfExperiment(possibleCompromises,riskDict,debug=True):
query = "networkConnectsTo(SourceService,TargetService,C*K,IOK,AOK)"
components = pyDatalog.ask(query).answers
getService0 = itemgetter(0)
getService1 = itemgetter(1)
componentsList = list(map(getService0,iter(components)))
componentsList2 = list(map(getService1,iter(components)))
componentsList.extend(componentsList2)
componentsList = list(set(componentsList))
#pprint.pprint(componentsList)
compromisedComponents = list(map(getService0,iter(possibleCompromises)))
possibleCompromisesDict = dict(possibleCompromises)
ledger = []
#pprint.pprint(compromisedComponents)
#print(possibleCompromisesDict.get("internet"))
#sumComponentUtilCompromised = 0
#sumComponentUtilUncompromised = 0
for component in componentsList:
newPossibleCompromises = possibleCompromisesDict
newPossibleCompromises[component] = 1.0
componentUtilCompromised = 1.0 #For code development only
#componentUtilCompromised = determineResidualUtility(newPossibleCompromises,riskDict,True)
newPossibleCompromises = possibleCompromisesDict
if component in compromisedComponents:
del newPossibleCompromises[component]
componentUtilUncompromised = 0.0 #For code development only
#componentUtilUncompromised = determineResidualUtility(newPossibleCompromises,riskDict,True)
ledger.append([component,componentUtilCompromised,componentUtilUncompromised])
pprint.pprint(ledger)
def __sonHermanos(_idiomaA, _nombreA, _idiomaB, _nombreB):
# Determinar si dos palabras son heman@s
if (ask('sonHermanos(' + _idiomaA + ',' + _nombreA + ',' + _idiomaB + ',' +
_nombreB + ', R)')):
return "Son hermanas"
else:
return "No son hermanas"
def executeQuery(query, console):
result = pyDatalog.ask(query)
console.print("|--------------------Query---------------------\n|", query)
console.print("|--------------------Result--------------------\n|", result)
if result == None:
return False
return True
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 __idiomasRelacionadosPalabra(_palabra):
# Listar los idiomas relacionados con una palabra
consulta = ask('_soloIdiomas(' + _palabra + ', R)')
if (consulta):
words = []
for r in consulta.answers:
words.append(r[0])
return words
else:
return "No hay idiomas relacionados"
def findall2(S,L):
"""
Hackish PROLOG findall emulation.
use cases: findall(C,V) -> Given a query String in S, ask the LogKB and resolve to a list of results in L.
"""
if (S.is_const() and not L.is_const()):
result = pyDatalog.ask(S.id).answers
result.sort()
yield (S,tuple(result))
else:
raise Exception("unhandled case in findall: {0},{1}".format(S.is_const(),L.is_const()))
def analize_user(self, user, prediction):
actions = []
self.__assert_user(user, prediction)
if ask('accept("{}")'.format(str(user.id))) != None:
actions.append('Accept')
else:
actions.append('Reject')
#if ask('log("{}")'.format(str(user.id))) != None:
# actions.append('Log')
return actions
def ask_predicate(self, predicate):
"""
Queries PyDataLog for the given predicate by constructing a query fitting the pyDataLog Syntax.
:param predicate: The predicate to be quried for
:return: The Value of the given predicate if it exists in the Database, None otherwise.
"""
query = predicate.name + "("
query += ','.join([str(a) if not isinstance(a, Symbol) else "\'" + str(a) + "\'" \
for a in predicate.args])
query += ", X)"
answer = pyDatalog.ask(query)
if answer is None:
return None
return self.transform_answer(answer.answers)
def ask(self, query_symbols, logical_query, coeff_expr=None):
"""
Builds a pyDataLog program from the logical_query and loads it. Then executes the query for the query_symbols.
:param query_symbols: The symbols to be queried.
:type query_symbols: list(SubSymbol)
:param logical_query:
:type:
:return:
"""
helper_len = 0
tmp = None
if not query_symbols:
return None
if coeff_expr is None:
helper_len = len(query_symbols)
helper_predicate = 'helper(' + ','.join([str(v) for v in query_symbols]) + ')'
tmp = helper_predicate + " <= " + self.transform_query(logical_query)
else:
helper_len = len(query_symbols) + 1
syms = OrderedSet(query_symbols)
syms.add('COEFF_EXPR')
helper_predicate = 'helper(' + ','.join([str(v) for v in syms]) + ')'
index_query = self.transform_query(logical_query)
coeff_query = "(COEFF_EXPR == " + str(coeff_expr) + ")"
if index_query is None:
tmp = helper_predicate + " <= " + coeff_query
else:
tmp = helper_predicate + " <= " + " & ".join([index_query, coeff_query])
log.debug("pyDatalog query: " + tmp)
pyDatalog.load(tmp)
answer = pyDatalog.ask(helper_predicate)
pyEngine.Pred.reset_clauses(pyEngine.Pred("helper", helper_len))
if answer is None:
return []
return self.transform_answer(answer.answers)
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 check_logic(thread_name, n):
query = pyDatalog.ask("queens(%s)" % (",".join("X%s" % i for i in range(n))))
answers = query.answers if query else []
result = "OK" if len(answers) == [1,0,0,2,10,4,40,92][n-1] else answers
print("%s : n = %d %s " % (thread_name, n, result))
Employee.indirect_manager(Mary, X)
print(X) # prints [John]
# Who are the employees of John with a salary below 6000 ?
result = (Employee.salary[X] < 6000) & Employee.indirect_manager(X, John)
print(result) # prints [(Sam,)]
print(X) # prints [Sam]
print((Employee.salary_class[X] == 5) & Employee.indirect_manager(X, John) >= X) # Sam
# verify that the manager of Mary is John
assert Employee.manager[Mary]==John
# who is his own indirect manager ?
Employee.indirect_manager(X, X)
print(X) # prints []
# who has 2 reports ?
Employee.report_count[X] == 2
print(X) # prints [John]
# what is the total salary of the employees of John ?
# note : it is better to place aggregation clauses in the class definition
pyDatalog.load("(Employee.budget[X] == sum(N, for_each=Y)) <= (Employee.indirect_manager(Y, X)) & (Employee.salary[Y]==N)")
Employee.budget[John]==X
print(X) # prints [12200]
# who has the lowest salary ?
pyDatalog.load("(lowest[1] == min(X, order_by=N)) <= (Employee.salary[X]==N)")
# must use ask() because inline queries cannot use unprefixed literals
print(pyDatalog.ask("lowest[1]==X")) # prints set([(1, 'Sam')])