clingexplaid 1.3.4

Tools to aid the development of explanation systems using clingo

from clingexplaid.transformers.transformer_assumption import FilterSignature

clingexplaid

API to aid the development of explanation systems using clingo

Installation

Clingo-Explaid easily be installed with pip:

pip install clingexplaid

Requirements

• python >= 3.9
• clingo >= 5.7.1

Building from Source

Please refer to DEVELOPEMENT

API

Here are two example for using clingexplaid's API.

Minimal Unsatisfiable Subsets (MUS)

Transforming facts to Assumptions (necessary pre-processing step):

from clingexplaid.preprocessors import AssumptionPreprocessor
from clingexplaid.preprocessors import (
    FilterSignature,
    FilterPattern,
)

PROGRAM = """
a(book;magazine;video).
b(test).
c(1..10).
d(1..3).
"""

ap = AssumptionPreprocessor(filters=[
    FilterSignature("a", 1),
    FilterPattern("d(2)")
])
result = ap.process(PROGRAM)
# You can either use the return value of `ap.process`
print(result)
# Or use `ap.control` with your transformed program already added
print(ap.control)

You can also use an existing control and pass it to the AssumptionPreprocessor as follows:

FILE = "local/encoding.lp"

ctl = clingo.Control("0")
ap = AssumptionPreprocessor(
    control=ctl,
    filters=[
    FilterSignature("a", 1),
    FilterPattern("d(2)")
])
ap.process_files([FILE])

# The transformed files are added to ctl so it can be directly used
ctl.ground([("base", [])])
ctl.solve()

Getting a single MUS:

from clingexplaid.preprocessors import AssumptionPreprocessor, FilterSignature
from clingexplaid.mus import CoreComputer

PROGRAM = """
a(1..3).
{b(4..6)}.

a(X) :- b(X).

:- a(X), X>=3.
"""

ap = AssumptionPreprocessor(filters={FilterSignature("a", 1)})
ap.process(PROGRAM)
ap.control.ground([("base", [])])
cc = CoreComputer(ap.control, ap.assumptions)

def shrink_on_core(core) -> None:
    mus_literals = cc.shrink(core)
    print("MUS:", cc.mus_to_string(mus_literals))

ap.control.solve(
    assumptions=list(ap.assumptions),
    on_core=shrink_on_core
)

Getting multiple MUS:

import clingo
from clingexplaid.transformers import AssumptionTransformer
from clingexplaid.mus import CoreComputer

PROGRAM = """
a(1..3).
b(1..3).

:- a(X), b(X).
"""

at = AssumptionTransformer()
transformed_program = at.parse_string(PROGRAM)
control = clingo.Control()
control.add("base", [], transformed_program)
control.ground([("base", [])])
assumptions = at.get_assumption_literals(control)
cc = CoreComputer(control, assumptions)

mus_generator = cc.get_multiple_minimal()
for i, mus in enumerate(mus_generator):
    print(f"MUS {i}:", cc.mus_to_string(mus))

Unsatisfiable Constraints

from clingexplaid.unsat_constraints import UnsatConstraintComputer

PROGRAM = """
a(1..3).
{b(4..6)}.

a(X) :- b(X).

:- a(X), X>=3.
"""

ucc = UnsatConstraintComputer()
ucc.parse_string(PROGRAM)
unsat_constraints = ucc.get_unsat_constraints()

for uc_id, unsat_constraint in unsat_constraints.items():
    print(f"Unsat Constraint {uc_id}:", unsat_constraint)