def propprInferenceCheck(self, weightVec, ruleStrings, modeString,
inputSymbol, expectedResultDict):
print 'testing inference for mode', modeString, 'on input', inputSymbol, 'with proppr rules:'
rules = parser.RuleCollection()
for r in ruleStrings:
rules.add(parser.Parser.parseRule(r))
prog = tensorlog.ProPPRProgram(db=self.db,
rules=rules,
weights=weightVec)
mode = tensorlog.ModeDeclaration(modeString)
fun = prog.compile(mode)
print 'native computation'
y1 = self.only(prog.eval(mode, [inputSymbol]))
self.checkDicts(self.db.rowAsSymbolDict(y1), expectedResultDict)
print 'theano computation'
thFun = prog.theanoPredictFunction(mode, ['x'])
y2 = self.only(thFun(self.db.onehot(inputSymbol)))
self.checkDicts(self.db.rowAsSymbolDict(y2), expectedResultDict)
def __init__(self, db=None, rules=parser.RuleCollection()):
self.db = db
self.program = []
self.function = {}
self.rules = rules
def __init__(self, db=None, rules=parser.RuleCollection(), weights=None):
super(ProPPRProgram, self).__init__(db=db, rules=rules)
self.weights = ops.columnVectorParam(weights, "weights")
assert featureLHS.arity == 1 and featureLHS.functor == 'all', 'non-constant features must be of the form {all(X):-...}'
outputVar = featureLHS.args[0]
return parser.Rule(parser.Goal('feature__', [inputVar, outputVar]),
rule.findall)
#
# sample main: python tensorlog.py test/fam.cfacts 'rel(i,o)' 'rel(X,Y):-spouse(X,Y).' william
#
if __name__ == "__main__":
if len(sys.argv) < 4:
print 'usage factfile rule1 ... mode x1 ...'
else:
db = matrixdb.MatrixDB.loadFile(sys.argv[1])
mode = None
rules = parser.RuleCollection()
xs = []
for a in sys.argv[2:]:
if a.find(":-") >= 0:
rules.add(parser.Parser.parseRule(a))
elif a.find("(") >= 0:
assert mode == None, 'only one mode allowed'
mode = ModeDeclaration(a)
else:
xs.append(a)
p = Program(db=db, rules=rules)
p.functionListing()
assert mode, 'mode must be defined'
f = p.compile(mode)
p.functionListing()
