"""Tests for response.py edge cases to increase coverage."""
import pytest
@pytest.mark.asyncio
class TestResponseEdgeCases:
"""Test edge cases in response building."""
async def test_zero_objective_value_gap_calculation(self):
"""Test optimality gap calculation when objective value is zero."""
from chuk_mcp_solver.models import (
LinearTerm,
Objective,
ObjectiveSense,
SearchConfig,
SolveConstraintModelRequest,
SolverMode,
Variable,
VariableDomain,
VariableDomainType,
)
from chuk_mcp_solver.solver.ortools import ORToolsSolver
# Problem where objective is zero: minimize x where x = 0
request = SolveConstraintModelRequest(
mode=SolverMode.OPTIMIZE,
variables=[
Variable(
id="x",
domain=VariableDomain(type=VariableDomainType.INTEGER, lower=0, upper=0),
)
],
constraints=[],
objective=Objective(sense=ObjectiveSense.MINIMIZE, terms=[LinearTerm(var="x", coef=1)]),
search=SearchConfig(max_time_ms=1000),
)
solver = ORToolsSolver()
response = await solver.solve_constraint_model(request)
assert response.status == "optimal"
assert response.objective_value == 0
async def test_feasible_solution_without_objective(self):
"""Test feasible solution summary when no objective is present."""
from chuk_mcp_solver.models import (
Constraint,
ConstraintKind,
ConstraintSense,
LinearConstraintParams,
LinearTerm,
SearchConfig,
SolveConstraintModelRequest,
SolverMode,
Variable,
VariableDomain,
VariableDomainType,
)
from chuk_mcp_solver.solver.ortools import ORToolsSolver
# Satisfiability problem with timeout to get FEASIBLE status
request = SolveConstraintModelRequest(
mode=SolverMode.SATISFY,
variables=[
Variable(
id="x",
domain=VariableDomain(type=VariableDomainType.INTEGER, lower=0, upper=1000),
)
],
constraints=[
Constraint(
id="c1",
kind=ConstraintKind.LINEAR,
params=LinearConstraintParams(
terms=[LinearTerm(var="x", coef=1)],
sense=ConstraintSense.GREATER_EQUAL,
rhs=5,
),
)
],
search=SearchConfig(max_time_ms=1), # Very short timeout
)
solver = ORToolsSolver()
response = await solver.solve_constraint_model(request)
# Should get either optimal or feasible (or timeout)
assert response.status in ("optimal", "feasible", "timeout_best", "satisfied")
async def test_feasible_with_objective_and_gap(self):
"""Test feasible solution with objective value and optimality gap."""
from chuk_mcp_solver.models import (
LinearTerm,
Objective,
ObjectiveSense,
SearchConfig,
SolveConstraintModelRequest,
SolverMode,
Variable,
VariableDomain,
VariableDomainType,
)
from chuk_mcp_solver.solver.ortools import ORToolsSolver
# Large problem with short timeout to get non-optimal solution
variables = [
Variable(
id=f"x{i}",
domain=VariableDomain(type=VariableDomainType.INTEGER, lower=0, upper=100),
)
for i in range(20)
]
request = SolveConstraintModelRequest(
mode=SolverMode.OPTIMIZE,
variables=variables,
constraints=[],
objective=Objective(
sense=ObjectiveSense.MAXIMIZE,
terms=[LinearTerm(var=f"x{i}", coef=1) for i in range(20)],
),
search=SearchConfig(max_time_ms=1), # Very short timeout
)
solver = ORToolsSolver()
response = await solver.solve_constraint_model(request)
# Should get a solution (optimal or feasible)
assert response.status in ("optimal", "feasible", "timeout_best")
