def test_computation_memory_two_constraints():
v1 = Variable("v1", list(range(10)))
v2 = Variable("v2", list(range(10)))
v3 = Variable("v3", list(range(10)))
v4 = Variable("v4", list(range(10)))
c1 = constraint_from_str("c1", " v1 == v2", [v1, v2])
c2 = constraint_from_str("c2", " v1 == v3", [v1, v3])
c3 = constraint_from_str("c3", " v1 == v4", [v1, v4])
v1_node = VariableComputationNode(v1, [c1, c2, c3])
# here, we have 3 edges , one for each constraint
assert mgm2.computation_memory(v1_node) == mgm2.UNIT_SIZE * 3 * 2
def three_variables_pb():
# a very simple problem with 3 variables
x1 = Variable("x1", ["R", "B"])
x2 = Variable("x2", ["R", "B"])
x3 = Variable("x3", ["R", "B"])
diff_x1_x2 = constraint_from_str("c1", "1 if x1 == x2 else 0", [x1, x2])
diff_x1_x3 = constraint_from_str("c2", "1 if x1 == x3 else 0", [x1, x3])
# build the pseudo-tree for this problem
g = build_computation_graph(None,
constraints=[diff_x1_x2, diff_x1_x3],
variables=[x1, x2, x3])
return g
def test_communication_load():
v1 = Variable('v1', list(range(10)))
v2 = Variable('v2', list(range(10)))
v3 = Variable('v3', list(range(10)))
v4 = Variable('v4', list(range(10)))
c1 = constraint_from_str('c1', ' v1 == v2', [v1, v2])
c2 = constraint_from_str('c2', ' v1 == v3', [v1, v3])
c3 = constraint_from_str('c3', ' v1 == v4', [v1, v4])
v1_node = VariableComputationNode(v1, [c1, c2, c3])
assert mgm2.UNIT_SIZE * 10 * 10 * 3 + mgm2.HEADER_SIZE \
== mgm2.communication_load(v1_node, 'v2')
def test_computation_memory_two_constraints():
v1 = Variable('v1', list(range(10)))
v2 = Variable('v2', list(range(10)))
v3 = Variable('v3', list(range(10)))
v4 = Variable('v4', list(range(10)))
c1 = constraint_from_str('c1', ' v1 == v2', [v1, v2])
c2 = constraint_from_str('c2', ' v1 == v3', [v1, v3])
c3 = constraint_from_str('c3', ' v1 == v4', [v1, v4])
v1_node = VariableComputationNode(v1, [c1, c2, c3])
# here, we have 3 edges , one for each constraint
assert dsa.computation_memory(v1_node) == dsa.UNIT_SIZE * 3
def test_communication_load():
v1 = Variable("v1", list(range(10)))
v2 = Variable("v2", list(range(10)))
v3 = Variable("v3", list(range(10)))
v4 = Variable("v4", list(range(10)))
c1 = constraint_from_str("c1", " v1 == v2", [v1, v2])
c2 = constraint_from_str("c2", " v1 == v3", [v1, v3])
c3 = constraint_from_str("c3", " v1 == v4", [v1, v4])
v1_node = VariableComputationNode(v1, [c1, c2, c3])
assert mgm2.UNIT_SIZE * 10 * 10 * 3 + mgm2.HEADER_SIZE == mgm2.communication_load(
v1_node, "v2")
def test_comp_creation_with_factory_method():
d = Domain("d", "", ["R", "G"])
v1 = Variable("v1", d)
v2 = Variable("v2", d)
c1 = constraint_from_str("c1", "10 if v1 == v2 else 0", [v1, v2])
graph = build_computation_graph(None, constraints=[c1], variables=[v1, v2])
comp_node = graph.computation("c1")
algo_def = AlgorithmDef.build_with_default_param("maxsum")
comp_def = ComputationDef(comp_node, algo_def)
comp = build_computation(comp_def)
assert comp is not None
assert comp.name == "c1"
assert comp.factor == c1
comp_node = graph.computation("v1")
algo_def = AlgorithmDef.build_with_default_param("maxsum")
comp_def = ComputationDef(comp_node, algo_def)
comp = build_computation(comp_def)
assert comp is not None
assert comp.name == "v1"
assert comp.variable.name == "v1"
assert comp.factors == ["c1"]
def build_models(light_domain, lights, max_model_size, model_count):
# Models: for each model
# * one constraint depends on 2 =< k =<max_model_size lights
# * one variable ,
# Example:
# function: 0 if 0.7 * l_d1 + 0.5 * l_d2 + 0.3 * l_lv3 == mv_desk else
# 1000
# function: '0 if 10 * abs(m0 - ( 0.2 * l1 + 0.5 * l2 + 0.8 * l3 )) < 3 else 1000'
models = {}
models_var = {}
for j in range(model_count):
model_var = Variable("m{}".format(j), domain=light_domain)
models_var[model_var.name] = model_var
model_size = randint(2, max_model_size)
model_lights = ()
light_expression_parts = []
for k, model_light in enumerate(sample(list(lights), model_size)):
impact = randint(1, 7) / 10
light_expression_parts.append(" {} * {}".format(model_light, impact))
# model_lights.append((model_light, impact))
# model_light.
light_expression = " + ".join(light_expression_parts)
model_expression = f"0 if 10* abs({model_var.name} - ({light_expression})) < 5 else 10000 ".format(
light_expression, model_var.name
)
model = constraint_from_str(
"c_m{}".format(j),
expression=model_expression,
all_variables=list(lights.values()) + [model_var],
)
models[model.name] = model
return models_var, models
def test_create_ok(self):
d1 = Domain('d1', '', [1, 2, 3, 5])
v1 = Variable('v1', d1)
f1 = constraint_from_str('f1', 'v1 * 0.5', [v1])
cv1 = VariableComputationNode(v1, [f1])
cf1 = FactorComputationNode(f1)
cg = ComputationsFactorGraph([cv1], [cf1])
def test_build_graph_from_variables_constraints():
d = Domain('d', 'test', [1, 2, 3])
v1 = Variable('v1', d)
v2 = Variable('v2', d)
v3 = Variable('v3', d)
c1 = constraint_from_str('c1', 'v1 * 0.5 + v2 - v3', [v1, v2, v3])
graph = build_computation_graph(variables=[v1, v2, v3], constraints=[c1])
def generate_unary_intentional_constraint(variable: Variable, un_range: float):
value = random.uniform(-un_range, un_range)
constraint = constraint_from_str(
name=f"cu_{variable.name}",
expression=f" -{value} if {variable.name} == 1 else {value}",
all_variables=[variable],
)
return constraint
def build_pb():
# A toy problem with 5 variables and 5 constraints.
# The objective here is to have a problem that is simple enough to be solved
# manually and used in test, but that is representative enough to be meaningful.
v_a = Variable("vA", ["R", "G"])
v_b = Variable("vB", ["R", "G"])
v_c = Variable("vC", ["R", "G"])
v_d = Variable("vD", ["R", "G"])
c1 = constraint_from_str(
"c1",
"{('R', 'G'): 8, "
" ('R', 'R'): 5, "
" ('G', 'G'): 3, "
" ('G', 'R'): 20 "
"}[(vA, vB)]",
[v_a, v_b],
)
c2 = constraint_from_str(
"c2",
"{('R', 'G'): 10, "
" ('R', 'R'): 5, "
" ('G', 'G'): 3, "
" ('G', 'R'): 20 "
"}[(vA, vC)]",
[v_a, v_c],
)
c3 = constraint_from_str(
"c3",
"{('R', 'G'): 4, "
" ('R', 'R'): 5, "
" ('G', 'G'): 3, "
" ('G', 'R'): 3 "
"}[(vB, vC)]",
[v_b, v_c],
)
c4 = constraint_from_str(
"c4",
"{('R', 'G'): 8, "
" ('R', 'R'): 3, "
" ('G', 'G'): 3, "
" ('G', 'R'): 10 "
"}[(vB, vD)]",
[v_b, v_d],
)
return [v_a, v_b, v_c, v_d], [c1, c2, c3, c4]
def test_computation_memory_one_constraint():
v1 = Variable("v1", list(range(10)))
v2 = Variable("v2", list(range(10)))
v3 = Variable("v3", list(range(10)))
c1 = constraint_from_str("c1", " v1 + v2 == v3", [v1, v2, v3])
v1_node = VariableComputationNode(v1, [c1])
# here, we have an hyper-edges with 3 vertices
assert mgm2.computation_memory(v1_node) == mgm2.UNIT_SIZE * 2 * 2
def test_create_node_with_custom_name():
d = Domain('d', 'test', [1, 2, 3])
v1 = Variable('v1', d)
c1 = constraint_from_str('c1', 'v1 * 0.5', [v1])
n1 = VariableComputationNode(v1, [c1], name='foo')
assert v1 == n1.variable
assert n1.name == 'foo'
def test_computation_memory_one_constraint():
v1 = Variable('v1', list(range(10)))
v2 = Variable('v2', list(range(10)))
v3 = Variable('v3', list(range(10)))
c1 = constraint_from_str('c1', ' v1 + v2 == v3', [v1, v2, v3])
v1_node = VariableComputationNode(v1, [c1])
# here, we have an hyper-edges with 3 vertices
assert dsa.computation_memory(v1_node) == dsa.UNIT_SIZE * 2
def test_raise_when_duplicate_computation_name(self):
d1 = Domain('d1', '', [1, 2, 3, 5])
v1 = Variable('v1', d1)
# here we create a relation with the same name as the variable
f1 = constraint_from_str('v1', 'v1 * 0.5', [v1])
cv1 = VariableComputationNode(v1, ['f1'])
cf1 = FactorComputationNode(f1)
self.assertRaises(KeyError, ComputationsFactorGraph, [cv1], [cf1])
def test_compute_factor_cost_at_start():
d = Domain("d", "", ["R", "G"])
v1 = Variable("v1", d)
v2 = Variable("v2", d)
c1 = constraint_from_str("c1", "10 if v1 == v2 else 0", [v1, v2])
obtained = factor_costs_for_var(c1, v1, {}, "min")
assert obtained["R"] == 0
assert obtained["G"] == 0
assert len(obtained) == 2
def test_create_node_no_neigbors():
d = Domain('d', 'test', [1, 2, 3])
v1 = Variable('v1', d)
c1 = constraint_from_str('c1', 'v1 * 0.5', [v1])
n1 = VariableComputationNode(v1, [c1])
assert v1 == n1.variable
assert c1 in n1.constraints
assert len(n1.links) == 1 # link to our-self
assert not n1.neighbors
def test_var_node_simple_repr():
d = Domain('d', 'test', [1, 2, 3])
v1 = Variable('v1', d)
c1 = constraint_from_str('c1', 'v1 * 0.5', [v1])
cv1 = VariableComputationNode(v1, [c1])
r = simple_repr(cv1)
cv1_obtained = from_repr(r)
assert cv1 == cv1_obtained
def test_create_node_with_binary_constraint():
d = Domain('d', 'test', [1, 2, 3])
v1 = Variable('v1', d)
v2 = Variable('v2', d)
c1 = constraint_from_str('c1', 'v1 * 0.5 - v2', [v1, v2])
n1 = VariableComputationNode(v1, [c1])
assert v1 == n1.variable
assert c1 in n1.constraints
assert list(n1.links)[0].has_node('v2')
assert 'v2' in n1.neighbors
def generate_binary_intentional_constraint(
variable1: Variable, variable2: Variable, bin_range: float
) -> Constraint:
value = random.uniform(-bin_range, bin_range)
constraint = constraint_from_str(
name=f"cb_{variable1.name}_{variable2.name}",
expression=f"{value} if {variable1.name} == {variable2.name} else -{value}",
all_variables=[variable1, variable2],
)
return constraint
def __add__(self,
info: Tuple[str, str, List[Variable]]):
"""Convenience notation for adding a constraint to te dcop.
Parameters
----------
info: a tuple name, expr_str, variables
name is the name of the constraint, as a string
expr_str is a python expression as a string, that return the
value of the constraint. names used in this expression must be
names of variables given in te third element of the tuple.
variable can be an iterable of variable object or a dictionary of
{name: variables}
Returns
-------
DCOP
the dcop itself, in order to be able to use the += notation.
Raises
------
ValueError
If a domain or variable with the same name, but not the same
definition, already exist for this dcop.
Notes
-----
Variable(s) and domain(s) involved in the constraint are automatically
added to the dcop.
See Also
--------
add_constraint
Examples
--------
>>> dcop = DCOP('test')
>>> v1 = Variable('v1', range(10) )
>>> dcop += 'c1', '2 if v1 > 5 else 10 ', [v1]
>>> dcop.constraints['c1'](8)
2
>>> dcop.constraints['c1'](1)
10
"""
(name, expr_str, variables) = info
if hasattr(variables, 'values'):
variables = variables.values()
c = constraint_from_str(name, expr_str, variables)
self.add_constraint(c)
return self
def test_variablenode_simple_repr():
d1 = Domain('d1', '', [1, 2, 3, 5])
v1 = Variable('v1', d1)
f1 = constraint_from_str('f1', 'v1 * 0.5', [v1])
cv1 = VariableComputationNode(v1, ['f1'])
cf1 = FactorComputationNode(f1, )
r = simple_repr(cv1)
obtained = from_repr(r)
assert obtained == cv1
assert cv1.variable == obtained.variable
def test_dcop_add_constraint_is_enough():
dcop = DCOP()
d = VariableDomain('test', 'test', values=range(10))
v1 = Variable('v1', d)
v2 = Variable('v2', d)
c1 = constraint_from_str('c1', '0 if v1 != v2 else 1000', [v1, v2])
dcop.add_constraint(c1)
assert dcop.constraints == {'c1': c1}
assert dcop.variables == {'v1': v1, 'v2': v2}
assert dcop.domains == {'test': d}
def test_graph_density():
d = Domain('d', 'test', [1, 2, 3])
v1 = Variable('v1', d)
v2 = Variable('v2', d)
v3 = Variable('v3', d)
c1 = constraint_from_str('c1', 'v1 * 0.5 + v2 - v3', [v1, v2, v3])
dcop = DCOP('dcop_test', 'min')
dcop.add_constraint(c1)
graph = build_computation_graph(dcop)
density = graph.density()
assert density == 1 / 3
def setUp(self):
variables = list(
create_variables('v', ['1', '2', '3'], Domain('d', '',
[1, 2])).values())
all_diff = constraint_from_str('all_diff', 'v1 + v2 + v3 ', variables)
v1, v2, v3 = variables
c1 = VariableComputationNode(v1, [all_diff])
c2 = VariableComputationNode(v2, [all_diff])
c3 = VariableComputationNode(v3, [all_diff])
nodes = [c1, c2, c3]
# links = [ConstraintLink('all_diff', ['c1', 'c2', 'c3'])]
self.cg = ComputationConstraintsHyperGraph(nodes)
self.agents = [AgentDef('a1'), AgentDef('a2'), AgentDef('a3')]
def build_lights(light_count, light_domain):
# Lights : cost function & variable
lights = {}
lights_cost = {}
for i in range(light_count):
light = Variable("l{}".format(i), domain=light_domain)
lights[light.name] = light
efficiency = randint(0, 90) / 100
cost = constraint_from_str(
"c_l{}".format(i),
expression="{} * {}".format(light.name, efficiency),
all_variables=[light],
)
lights_cost[cost.name] = cost
return lights, lights_cost
def test_no_neighbors():
x1 = Variable("x1", list(range(10)))
cost_x1 = constraint_from_str('cost_x1', 'x1 *2 ', [x1])
computation = Mgm2Computation(x1, [cost_x1],
mode='max',
comp_def=MagicMock())
computation.value_selection = MagicMock()
computation.finished = MagicMock()
vals, cost = computation._compute_best_value()
assert cost == 18
assert set(vals) == {9}
computation.on_start()
computation.value_selection.assert_called_once_with(9, 18)
computation.finished.assert_called_once_with()
def test_link_simple_repr():
d = Domain('d', 'test', [1, 2, 3])
v1 = Variable('v1', d)
v2 = Variable('v2', d)
v3 = Variable('v3', d)
c1 = constraint_from_str('c1', 'v1 * 0.5 + v2 - v3', [v1, v2, v3])
cv1 = VariableComputationNode(v1, [c1])
cv2 = VariableComputationNode(v2, [c1])
cv3 = VariableComputationNode(v3, [c1])
link = ConstraintLink(c1.name, ['c1', 'c2', 'c3'])
r = simple_repr(link)
link_obtained = from_repr(r)
assert link == link_obtained
def test_problem_with_non_binary_constraints_raises_exception():
d = Domain("values", "", [1, 0])
v1 = Variable("v1", d)
v2 = Variable("v2", d)
v3 = Variable("v3", d)
c1 = constraint_from_str("c1", "v1 + v2 + v3 <= 2", [v1, v2, v3])
g = build_computation_graph(None, constraints=[c1], variables=[v1, v2, v3])
with pytest.raises(ComputationException) as comp_exc:
get_computation_instance(g, "v1")
assert f" with arity {3}" in str(comp_exc.value)
with pytest.raises(ComputationException) as comp_exc:
get_computation_instance(g, "v3")
assert f" with arity {3}" in str(comp_exc.value)
with pytest.raises(ComputationException) as comp_exc:
get_computation_instance(g, "v2")
assert f" with arity {3}" in str(comp_exc.value)
def test_no_neighbors():
x1 = Variable("x1", list(range(10)))
cost_x1 = constraint_from_str("cost_x1", "x1 *2 ", [x1])
computation = Mgm2Computation(
ComputationDef(
VariableComputationNode(x1, [cost_x1]),
AlgorithmDef.build_with_default_param("mgm2", mode="max"),
))
computation.value_selection = MagicMock()
computation.finished = MagicMock()
vals, cost = computation._compute_best_value()
assert cost == 18
assert set(vals) == {9}
computation.on_start()
computation.value_selection.assert_called_once_with(9, 18)
computation.finished.assert_called_once_with()