Runtime questions

[loc-trinh]

Hi there, sorry for another question:

This is the full program for the Missle logic program:

from lnn import (Predicate, Variable, Join, And,
                 Exists, Implies, ForAll, Model, Fact, World)

model = Model()  # Instantiate a model.
x, y, z, w = map(Variable, ['x', 'y', 'z', 'w'])

missile = Predicate('missile')
american = Predicate('american')
hostile = Predicate('hostile')
criminal = Predicate('criminal')
weapon = Predicate('weapon')
owns = Predicate('owns', 2)    # binary predicate
enemy = Predicate('enemy', 2)  # binary predicate
sells = Predicate('sells', 3)  # ternary predicate

americaenemyishostile = ForAll(x, Implies(enemy(x, 'america'), hostile(x)))
missleareweapon = ForAll(x, Implies(missile(x), weapon(x)))
nonomisslesoldbywest = ForAll(x, Implies(And(missile(x), owns('nono', x)),sells('west', x, 'nono')))
youarecriminalif = ForAll(x,y,z, Implies(And(american(x), sells(x,y,z), weapon(y), hostile(z)), criminal(x)))

query = Exists(x, criminal(x))
model.add_knowledge(americaenemyishostile, missleareweapon, nonomisslesoldbywest, youarecriminalif, query)

model.add_data({
    owns: {('nono', 'm1'): Fact.TRUE},
    missile: {'m1': Fact.TRUE},
    american: {'west': Fact.TRUE},
    enemy: {('nono', 'america'): Fact.TRUE, ('wakanda', 'america'): Fact.TRUE, ('gotham', 'america'): Fact.TRUE},
})

model[query].print()
model.infer()
model[query].print()
model[query].true_groundings

Running with

model.add_data({
    owns: {('nono', 'm1'): Fact.TRUE},
    missile: {'m1': Fact.TRUE},
    american: {'west': Fact.TRUE},
    enemy: {('nono', 'america'): Fact.TRUE, ('wakanda', 'america'): Fact.TRUE, ('gotham', 'america'): Fact.TRUE},
})

yields the correct result in less than 2s:

OPEN Exists: (∃0, criminal(0))                              TRUE (1.0, 1.0)

{'west'}

However, running with two extra facts that should not impact the results

model.add_data({
    owns: {('nono', 'm1'): Fact.TRUE}, ('wakanda', 'm2'): Fact.TRUE}, ('gotham', 'm3'): Fact.TRUE}
    missile: {'m1': Fact.TRUE, 'm2': Fact.TRUE, 'm3': Fact.TRUE},
    american: {'west': Fact.TRUE},
    enemy: {('nono', 'america'): Fact.TRUE, ('wakanda', 'america'): Fact.TRUE, ('gotham', 'america'): Fact.TRUE},
})

stalls for > 1hr.

Is this common and do you know if LNN in general will be able to scale to much larger problems?

[NaweedAghmad]

Thanks for bringing this to our attention.

@KyleErwin I've tested the first bit of code above, and it seems that #71 has even slowed down inference time of the original 2s query. Grounding management seems to be looping for an excessive amount of time here, might be an edge case that pandas is running into.

Some notes on the above code:

• the new API requires all root universals to be set as an axiom.
• I also had to turn off the FOL duplicates checking in the node aggregation, I'm not confident that it's been unit tested thoroughly enough.