import sys
from dataclasses import asdict, is_dataclass
from datetime import timedelta
from typing import Any, Dict, Iterable, List, Optional, Sequence, Union
import minizinc
if sys.version_info >= (3, 8):
from typing import Protocol
class DataClass(Protocol):
# Checking for this attribute is currently the most reliable way to
# ascertain that something is a dataclass
__dataclass_fields__: Dict
else:
DataClass = Any
[docs]
def check_result(
model: minizinc.Model,
result: minizinc.Result,
solver: minizinc.Solver,
solution_nrs: Optional[Sequence[int]] = None,
) -> bool:
"""Checks a result object for a model using the given solver.
Check the correctness of the solving process using a (different) solver
configuration. The solver configuration is now used to confirm is
assignment of the variables is correct. By default only the last solution
will be checked. A sequence of solution numbers can be provided to check
multiple solutions.
Args:
model (Model): To model for which the solution was provided
result (Result): The solution to be checked
solver (Solver): The solver configuration used to check the
solutions.
solution_nrs: The index set of solutions to be checked. (default:
``-1``)
Returns:
bool: True if the given result object is correctly verified.
"""
if solution_nrs is None:
solution_nrs = [-1]
solutions = (
result.solution
if isinstance(result.solution, list)
else [result.solution]
)
for i in solution_nrs:
sol = solutions[i]
if not check_solution(model, sol, result.status, solver):
return False
return True
class TimeoutError(Exception):
"""Exception raised for timeout errors (UNKNOWN status) when checking solutions"""
pass
[docs]
def check_solution(
model: minizinc.Model,
solution: Union[DataClass, Dict[str, Any]],
status: minizinc.Status,
solver: minizinc.Solver,
time_limit: Optional[timedelta] = timedelta(seconds=30),
) -> bool:
"""Checks a solution for a model using the given solver.
Check the correctness of the solving process using a (different) solver
configuration. A new model instance is created and will be assigned all
available values from the given solution. The Instance.solve() method is
then used to ensure that the same solution with the same expected status is
reached. Note that this method will not check the optimality of a solution.
Args:
model (Model): The model for which the solution was provided.
solution (Union[DataClass, Dict[str, Any]]): The solution to be checked.
status (Status): The expected (compatible) MiniZinc status.
solver (Solver): The solver configuration used to check the
solution.
time_limit (Optional(timedelta)): An optional time limit to check the
solution.
Returns:
bool: True if the given solution are correctly verified.
Raises:
TimeoutError: the given time limit was exceeded.
"""
instance = minizinc.Instance(solver, model)
if is_dataclass(solution):
solution = asdict(solution)
assert isinstance(solution, dict)
for k, v in solution.items():
if k not in ("objective", "_output_item", "_checker"):
instance[k] = v
check = instance.solve(time_limit=time_limit)
if check.status is minizinc.Status.UNKNOWN:
raise TimeoutError(
f"Solution checking failed because the checker exceeded the allotted time limit of {time_limit}"
)
elif status == check.status:
return True
return check.status in [
minizinc.Status.SATISFIED,
minizinc.Status.OPTIMAL_SOLUTION,
] and status in [
minizinc.Status.SATISFIED,
minizinc.Status.OPTIMAL_SOLUTION,
minizinc.Status.ALL_SOLUTIONS,
]
def _add_diversity_to_opt_model(
inst: minizinc.Instance,
obj_annots: Dict[str, Any],
vars: List[Dict[str, Any]],
sol_fix: Optional[Dict[str, Iterable]] = None,
):
for var in vars:
# Current and previous variables
varname = var["name"]
varprevname = var["prev_name"]
# Add the 'previous solution variables'
inst[varprevname] = []
# Fix the solution to given once
if sol_fix is not None:
inst.add_string(
f"constraint {varname} == {list(sol_fix[varname])};\n"
)
# Add the optimal objective.
if obj_annots["sense"] != "0":
obj_type = obj_annots["type"]
inst.add_string(f"{obj_type}: div_orig_opt_objective :: output;\n")
inst.add_string(
f"constraint div_orig_opt_objective == {obj_annots['name']};\n"
)
if obj_annots["sense"] == "-1":
inst.add_string(f"solve minimize {obj_annots['name']};\n")
else:
inst.add_string(f"solve maximize {obj_annots['name']};\n")
else:
inst.add_string("solve satisfy;\n")
return inst
def _add_diversity_to_div_model(
inst: minizinc.Instance,
vars: List[Dict[str, Any]],
div_anns: Dict[str, Any],
gap: Union[int, float],
sols: Dict[str, Any],
):
# Add the 'previous solution variables'
for var in vars:
# Current and previous variables
varname = var["name"]
varprevname = var["prev_name"]
varprevisfloat = "float" in var["prev_type"]
distfun = var["distance_function"]
prevsols = sols[varprevname] + [sols[varname]]
prevsol = (
__round_elements(prevsols, 6) if varprevisfloat else prevsols
) # float values are rounded to six decimal places to avoid infeasibility due to decimal errors.
# Add the previous solutions to the model code.
inst[varprevname] = prevsol
# Add the diversity distance measurement to the model code.
dim = __num_dim(prevsols)
dotdots = ", ".join([".." for _ in range(dim - 1)])
varprevtype = "float" if "float" in var["prev_type"] else "int"
inst.add_string(
f"array [1..{len(prevsol)}] of var {varprevtype}: dist_{varname} :: output = [{distfun}({varname}, {varprevname}[sol,{dotdots}]) | sol in 1..{len(prevsol)}];\n"
)
# Add minimum distance to the diversity distance measurement in the model code
if var["lb"] != "infinity":
inst.add_string(
f"constraint forall(sol in 1..{len(prevsol)})( dist_{varname}[sol] >= {var['lb']});"
)
# Add maximum distance to the diversity distance measurement in the model code
if var["ub"] != "infinity":
inst.add_string(
f"constraint forall(sol in 1..{len(prevsol)})( dist_{varname}[sol] <= {var['ub']});"
)
obj_sense = div_anns["objective"]["sense"]
aggregator = (
div_anns["aggregator"] if div_anns["aggregator"] != "" else "sum"
)
combinator = (
div_anns["combinator"] if div_anns["combinator"] != "" else "sum"
)
# Add the bound on the objective.
if obj_sense == "-1":
inst.add_string(f"constraint div_orig_objective <= {gap};\n")
elif obj_sense == "1":
inst.add_string(f"constraint div_orig_objective >= {gap};\n")
# Add new objective: maximize diversity.
div_combinator = ", ".join(
[f"{var['coef']} * dist_{var['name']}[sol]" for var in vars]
)
dist_total = f"{aggregator}([{combinator}([{div_combinator}]) | sol in 1..{len(prevsol)}])"
inst.add_string(f"solve maximize {dist_total};\n")
return inst
def __num_dim(x: List) -> int:
i = 1
while isinstance(x[0], list):
i += 1
x = x[0]
return i
def __round_elements(x: List, p: int) -> List:
for i in range(len(x)):
if isinstance(x[i], list):
x[i] = __round_elements(x[i], p)
elif isinstance(x[i], float):
x[i] = round(x[i], p)
return x