def test_dcop_add_agents_from_list():
dcop = DCOP()
a1 = AgentDef('a1')
a2 = AgentDef('a2')
dcop.add_agents([a1, a2])
assert dcop.agent('a1').name == 'a1'
assert dcop.agent('a2').name == 'a2'
def generate_ising(args):
domain_size = args.range
size = args.size
d = VariableDomain('d', 'dummy', range(domain_size))
variables = {}
constraints = {}
for i in range(size):
for j in range(size):
v = Variable('v{}_{}'.format(i, j), d,
floor(domain_size * random.random()))
variables[(i, j)] = v
for i, j in variables:
c = _create_ising_constraint(i, j, i, (j + 1) % size, domain_size,
variables)
constraints[(i, j, i, (j + 1) % size)] = c
c = _create_ising_constraint(i, j, (i + 1) % size, j, domain_size,
variables)
constraints[(i, j, (i + 1) % size), j] = c
dcop = DCOP('radom ising', 'min')
#dcop.domains = {'d': d}
#dcop.variables = variables
dcop._agents_def = {}
for c in constraints.values():
dcop.add_constraint(c)
if args.output:
outputfile = args.output[0]
write_in_file(outputfile, dcop_yaml(dcop))
else:
print(dcop_yaml(dcop))
def test_build_single_var(self):
v1 = Variable('v1', [1, 2, 3])
dcop = DCOP('test', 'min')
dcop.variables = {'v1': v1}
cg = build_computation_graph(dcop)
self.assertEqual(len(cg.nodes), 1)
self.assertEqual(cg.computation('v1').variable, v1)
self.assertEqual(len(cg.links), 0)
def test_density_two_var_one_factor(self):
dcop = DCOP('test', 'min')
d1 = VariableDomain('d1', '--', [1, 2, 3])
v1 = Variable('v1', d1)
v2 = Variable('v2', d1)
c1 = relation_from_str('c1', '0.5 * v1 + v2', [v1, v2])
dcop.add_constraint(c1)
g = build_computation_graph(dcop)
self.assertEqual(g.density(), 2 / 2)
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 test_filter_dcop():
dcop = DCOP()
d = VariableDomain('test', 'test', values=range(10))
v1 = Variable('v1', d)
v2 = Variable('v2', d)
v3 = Variable('v3', d)
dcop += 'c1', '0 if v1 != v2 else 1000', [v1, v2]
dcop.add_variable(v3)
assert dcop.variables == {'v1': v1, 'v2': v2, "v3": v3}
filtered = filter_dcop(dcop)
assert filtered.variables == {'v1': v1, 'v2': v2}
def test_api_dcop_graph_coloring():
# Graph coloring with 3 variables and color preferences
dcop = DCOP('test')
# Domain and variables
d = Domain('color', '', ['R', 'G'])
variables = create_variables('v', [1, 2, 3], d)
# Creating constraints using the convenience += notation
# We could also use the more verbose dcop.add_constraint method.
# Variable(s) and domain(s) involved in the constraint are automatically
# added to the dcop.
# unary constraints for preferences
dcop += 'cost_1', '-0.1 if v1 == "R" else 0.1 ', variables
dcop += 'cost_2', '-0.1 if v2 == "G" else 0.1 ', variables
dcop += 'cost_3', '-0.1 if v3 == "G" else 0.1 ', variables
# coloring constraints : v1 != v2 != v3
dcop += 'c1', '1 if v1 == v2 else 0', variables
dcop += 'c2', '1 if v3 == v2 else 0', variables
# let's check the dcop really has all required domain, variables and
# constraints
assert len(dcop.variables) == 3
assert 'v1' in dcop.variables
assert len(dcop.domains) == 1
assert 'color' in dcop.domains
assert len(dcop.constraints) == 5
assert 'cost_3' in dcop.constraints
assert 'c2' in dcop.constraints
def test_solve_max(toy_pb):
variables, constraints = toy_pb
dcop = DCOP(name="toy",
variables={v.name: v
for v in variables},
constraints={c.name: c
for c in constraints},
objective="max")
dcop.add_agents(create_agents("a", [1, 2, 3, 4]))
assignment = solve(dcop, "syncbb", "oneagent")
# Note: this is supposed to be exactly the same pb as bellow
assert assignment == {"vA": "G", "vB": "R", "vC": "R", "vD": "G"}
def test_build_two_var(self):
v1 = Variable('v1', [1, 2, 3])
v2 = Variable('v2', [1, 2, 3])
c1 = relation_from_str('c1', 'v1 + v2 ', [v1, v2])
dcop = DCOP('test', 'min')
dcop.add_constraint(c1)
cg = build_computation_graph(dcop)
self.assertEqual(len(cg.nodes), 2)
self.assertEqual(cg.computation('v1').variable, v1)
self.assertEqual(cg.computation('v2').variable, v2)
# one parent link, one children link
self.assertEqual(len(cg.links), 2)
def generate_ising(
row_count: int,
col_count: int,
bin_range: float,
un_range: float,
extensive: bool,
no_agents: bool,
fg_dist: bool,
var_dist: bool,
) -> Tuple[DCOP, Dict, Dict]:
grid_graph = nx.grid_2d_graph(row_count, col_count, periodic=True)
domain = Domain("var_domain", "binary", [0, 1])
variables = generate_binary_variables(grid_graph, domain)
constraints = {}
unary_constraints = generate_unary_constraints(variables, un_range, extensive)
constraints.update(unary_constraints)
binary_constraints = generate_binary_constraints(
grid_graph, variables, bin_range, extensive
)
constraints.update(binary_constraints)
agents = {}
fg_mapping = defaultdict(lambda: [])
var_mapping = defaultdict(lambda: [])
for (row, col) in grid_graph.nodes:
agent = AgentDef(f"a_{row}_{col}")
agents[agent.name] = agent
left = (row - 1) % row_count
down = (col + 1) % col_count
if var_dist:
var_mapping[agent.name].append(f"v_{row}_{col}")
if fg_dist:
fg_mapping[agent.name].append(f"v_{row}_{col}")
fg_mapping[agent.name].append(f"cu_v_{row}_{col}")
# Sort coordinate to make sure we build the name in the same order as when
# creating the constraints:
(r1, c1), (r2, c2) = sorted([(row, col), (left, col)])
fg_mapping[agent.name].append(f"cb_v_{r1}_{c1}_v_{r2}_{c2}")
(r1, c1), (r2, c2) = sorted([(row, col), (row, down)])
fg_mapping[agent.name].append(f"cb_v_{r1}_{c1}_v_{r2}_{c2}")
name = f"Ising_{row_count}_{col_count}_{bin_range}_{un_range}"
if no_agents:
agents = {}
dcop = DCOP(
name,
domains={"var_domain": domain},
variables={v.name: v for v in variables.values()},
agents=agents,
constraints=constraints,
)
return dcop, dict(var_mapping), dict(fg_mapping)
def test_solve_min(toy_pb):
variables, constraints = toy_pb
dcop = DCOP(name="toy",
variables={v.name: v
for v in variables},
constraints={c.name: c
for c in constraints},
objective="min")
dcop.add_agents(create_agents("a", [1, 2, 3, 4]))
assignment = solve(dcop, "syncbb", "oneagent")
# Note: this is exactly the same pb as in the file bellow
# dcop = load_dcop_from_file(["/pyDcop/tests/instances/graph_coloring_tuto.yaml"])
assert assignment == {"vA": "G", "vB": "G", "vC": "G", "vD": "G"}
def test_density_two_var_one_factor():
dcop = DCOP('test', 'min')
d1 = Domain('d1', '--', [1, 2, 3])
v1 = Variable('v1', d1)
v2 = Variable('v2', d1)
dcop += 'c1', '0.5 * v1 + v2', [v1, v2]
g = build_computation_graph(dcop)
assert g.density() == 4 / 6
def test_one_var_one_factor():
dcop = DCOP('test', 'min')
d1 = Domain('d1', '--', [1, 2, 3])
v1 = Variable('v1', d1)
dcop += 'c1', '0.5 * v1', [v1]
g = build_computation_graph(dcop)
assert len(g.links) == 1
assert len(g.nodes) == 2
def test_filter_dcop_unary_constraint_accepted():
dcop = DCOP()
d = VariableDomain('test', 'test', values=range(10))
v1 = Variable('v1', d)
v2 = Variable('v2', d)
v3 = Variable('v3', d)
v4 = Variable('v4', d)
dcop += 'c1', '0 if v1 != v2 else 1000', [v1, v2]
# unary constraint on V3
dcop += 'c2', 'v3 * 0.5', [v3]
dcop.add_variable(v4)
filtered = filter_dcop(dcop, accept_unary=False)
# c2 and v3 must stay here, as we accept unary constraints, but v4 must be gone
assert "v3" in filtered.variables
assert "c2" in filtered.constraints
assert "v4" not in filtered.variables
def test_dcop_add_constraint_easy_api():
dcop = DCOP()
d = VariableDomain('test', 'test', values=range(10))
v1 = Variable('v1', d)
v2 = Variable('v2', d)
dcop += 'c1', '0 if v1 != v2 else 1000', [v1, v2]
assert 'c1' in dcop.constraints
assert dcop.variables == {'v1': v1, 'v2': v2}
assert dcop.domains == {'test': d}
def test_3nodes_tree_cycle(self):
domain = ['a', 'b', 'c']
x1 = Variable('x1', domain)
x2 = Variable('x2', domain)
x3 = Variable('x3', domain)
variables = [x1, x2, x3]
r1 = NAryFunctionRelation(lambda x, y: x + y, [x1, x2], name='r1')
r2 = NAryFunctionRelation(lambda x, y: x + y, [x1, x3], name='r2')
r3 = NAryFunctionRelation(lambda x, y: x + y, [x2, x3], name='r3')
relations = [r1, r2, r3]
dcop = DCOP('test', 'min')
dcop.add_constraint(r1)
dcop.add_constraint(r2)
dcop.add_constraint(r3)
cg = build_computation_graph(dcop)
self.assertEqual(len(cg.nodes), len(variables))
self.assertEqual(len(cg.roots), 1)
# All variables have the same number of neighbors, they could all be
# root
self.assertIn(cg.roots[0].variable, [x1, x2, x3])
self.assertEqual(cg.roots[0].parent, None)
root = _generate_dfs_tree(variables, relations, root=x1)
self.assertEqual(root.variable, x1)
self.assertEqual(root.parent, None)
def test_dcop_add_agents_from_dict():
dcop = DCOP()
agts = create_agents('a', [1, 2, 3])
dcop.add_agents(agts)
assert dcop.agent('a1').name == 'a1'
assert dcop.agent('a2').name == 'a2'
assert dcop.agent('a3').name == 'a3'
def create_dcop():
dcop = DCOP('test')
# Domain and vraibales
d = Domain('color', '', ['R', 'G'])
variables = create_variables('v', [1, 2, 3], d)
# unary constraints for preferences
dcop += 'cost_1', '-0.1 if v1 == "R" else 0.1 ', variables
dcop += 'cost_2', '-0.1 if v2 == "G" else 0.1 ', variables
dcop += 'cost_3', '-0.1 if v3 == "G" else 0.1 ', variables
# coloring constraints : v1 != v2 != v3
dcop += 'c1', '1 if v1 == v2 else 0', variables
dcop += 'c2', '1 if v3 == v2 else 0', variables
return dcop
def load_dcop(dcop_str: str) -> DCOP:
loaded = yaml.load(dcop_str)
if 'name' not in loaded:
raise ValueError('Missing name in dcop string')
if 'objective' not in loaded or loaded['objective'] not in ['min', 'max']:
raise ValueError('Objective is mandatory and must be min or max')
dcop = DCOP(loaded['name'], loaded['objective'],
loaded['description'] if 'description' in loaded else '')
dcop.domains = _build_domains(loaded)
dcop.variables = _build_variables(loaded, dcop)
dcop.external_variables = _build_external_variables(loaded, dcop)
dcop._constraints = _build_constraints(loaded, dcop)
dcop._agents_def = _build_agents(loaded)
dcop.dist_hints = _build_dist_hints(loaded, dcop)
return dcop
def generate_secp(args):
logger.info("Generate SECP %s", args)
light_count = args.lights
model_count = args.models
rule_count = args.rules
capacity = args.capacity
max_model_size = args.max_model_size
max_rule_size = args.max_rule_size
light_domain = Domain("light", "light", range(0, 5))
lights_var, lights_cost = build_lights(light_count, light_domain)
models_var, models_constraints = build_models(
light_domain, lights_var, max_model_size, model_count
)
rules_constraints = build_rules(rule_count, lights_var, models_var, max_rule_size)
# Agents : one for each light
agents = build_agents(lights_var, lights_cost, capacity)
# Force
# * each light variable to be hosted on the corresponding agent
# * model constraint and var are preferred on the same agent
variables = lights_var.copy()
variables.update(models_var)
constraints = models_constraints.copy()
constraints.update(lights_cost)
constraints.update(rules_constraints)
dcop = DCOP(
"graph coloring",
"min",
domains={"light_domain": light_domain},
variables=variables,
agents=agents,
constraints=constraints,
)
if args.output:
outputfile = args.output
write_in_file(outputfile, dcop_yaml(dcop))
else:
print(dcop_yaml(dcop))
def load_dcop(dcop_str: str) -> DCOP:
loaded = yaml.load(dcop_str, Loader=yaml.FullLoader)
if "name" not in loaded:
raise ValueError("Missing name in dcop string")
if "objective" not in loaded or loaded["objective"] not in ["min", "max"]:
raise ValueError("Objective is mandatory and must be min or max")
dcop = DCOP(
loaded["name"],
loaded["objective"],
loaded["description"] if "description" in loaded else "",
)
dcop.domains = _build_domains(loaded)
dcop.variables = _build_variables(loaded, dcop)
dcop.external_variables = _build_external_variables(loaded, dcop)
dcop._constraints = _build_constraints(loaded, dcop)
dcop._agents_def = _build_agents(loaded)
dcop.dist_hints = _build_dist_hints(loaded, dcop)
return dcop
def test_filter_dcop_unary_constraint():
dcop = DCOP()
d = VariableDomain('test', 'test', values=range(10))
v1 = Variable('v1', d)
v2 = Variable('v2', d)
v3 = Variable('v3', d)
dcop += 'c1', '0 if v1 != v2 else 1000', [v1, v2]
# unary constraint on V3
dcop += 'c2', 'v3 * 0.5', [v3]
assert dcop.variables == {'v1': v1, 'v2': v2, "v3": v3}
filtered = filter_dcop(dcop)
# v3 is only involved in unary constraints c2 => v3 and c2 should be gone
assert filtered.variables == {'v1': v1, 'v2': v2}
assert "c2" not in filtered.constraints
def test_build_graph_from_dcop():
d = Domain('d', 'test', [1, 2, 3])
v1 = Variable('v1', d)
v2 = Variable('v2', d)
v3 = Variable('v3', d)
dcop = DCOP('dcop_test', 'min')
dcop += 'c1', 'v1 * 0.5 + v2 - v3', [v1, v2, v3]
c1 = dcop.constraints['c1']
graph = build_computation_graph(dcop)
links = list(graph.links)
assert 1 == len(links)
assert ConstraintLink(c1.name, {'v1', 'v2', 'v3'}) in links
nodes = list(graph.nodes)
assert 3 == len(nodes)
assert VariableComputationNode(v1, [c1]) in nodes
assert VariableComputationNode(v2, [c1]) in nodes
assert VariableComputationNode(v3, [c1]) in nodes
def dcop_graphcoloring_3():
"""
Build a simple 3-variable graph coloring problem.
v1--v2--v3
Each variable has a cost function which makes it prefer one color:
v1 prefers R
v2 prefers G
v3 prefers G
Of course, the preferences of v2 and v3 conflict.
The best affectation is
v1 - R
v2 - G
v3 - R
Returns
-------
"""
dcop = DCOP('graphcoloring_3')
# Domain and variables
d = Domain('color', '', ['R', 'G'])
variables = create_variables('v', ['1', '2', '3'], d)
# unary constraints for preferences
dcop += 'cost_1', '-0.1 if v1 == "R" else 0.1 ', variables
dcop += 'cost_2', '-0.1 if v2 == "G" else 0.1 ', variables
dcop += 'cost_3', '-0.1 if v3 == "G" else 0.1 ', variables
# coloring constraints : v1 != v2 != v3
dcop += 'c1', '1 if v1 == v2 else 0', variables
dcop += 'c2', '1 if v3 == v2 else 0', variables
# Note that we do not define the agents for this dcop here,
# as the number of agents depends on the test case we will use the dcop for.
return dcop
def generate_mixed_problem(args):
logger.debug('generate_mixed_problem %s ', args)
variable_count = args.variable_count
constraint_count = args.constraint_count
density = args.density
real_density = density
domain_range = args.range
arity = args.arity
auto_agents = args.agents is None
capacity = args.capacity
agents_count = variable_count if auto_agents else args.agents
nb_max_edges = constraint_count * min(arity, variable_count)
edges_count = int(nb_max_edges * density)
hard_count = int(args.hard_constraint * edges_count)
logger.info(
'Generating random DCOP graph with %s variables, whose domain '
'are [0;%s], %s edges, %s agents, %s hard '
'constraints and %s soft constraints', variable_count,
domain_range - 1, edges_count, agents_count, hard_count,
constraint_count - hard_count)
if arity > variable_count:
raise ValueError(
"The arity of a constraint must be at most the "
"number of variable. Arity: {}, Nb variables: {}".format(
arity, variable_count))
if hard_count < 0:
raise ValueError(
"The argument '-h' (or '--hard_count') must be "
"between 0 and 1. Currently set to: {}".format(hard_count))
# Create sets for the bipartite graph
if constraint_count <= 0:
raise ValueError(
"The argument '-c' (or '--constraint_count') must be "
"strictly positive. Currently set to: {}".format(constraint_count))
if variable_count < 0:
raise ValueError(
"The argument '-v' (or '--variable_count') must be "
"at least 1. Currently set to: {}".format(variable_count))
if arity <= 0:
raise ValueError("The argument '-a' (or '--arity') must be "
"at least 1. Currently set to: {}".format(arity))
d = VariableDomain('levels', 'level', range(domain_range))
variables = {}
agents = {}
constraints = {}
if arity == 1:
if constraint_count != variable_count:
raise ValueError("For max arity 1 you need the same number of "
"variables, constraints and edges. You asked "
"for {} variables and {} constraints.".format(
variable_count, constraint_count))
nodes = [i + 1 for i in range(variable_count)]
constraints_list = [("c{}".format(i + 1),
'hard') if i < hard_count else
("c{}".format(i + 1), 'soft')
for i in range(constraint_count)]
variables = {}
constraints = {}
while len(nodes) != 0:
n = nodes.pop()
c = constraints_list.pop(
random.randint(0,
len(constraints_list) - 1))
w = choose_weight()
hard = c[1] == 'hard'
objective = find_objective([w], domain_range - 1, hard)
if hard:
expression = "float('inf') if " + str(w) + "*v" + str(n) +\
" != " + str(objective) + " else 0"
else:
expression = str(w) + "*v" + str(n) + " - " + str(objective)
v = Variable("v" + str(n), d)
variables["v" + str(n)] = v
constraints[c[0]] = relation_from_str(c[0], expression, [v])
if auto_agents:
a_name = 'a' + str(n)
agents[a_name] = AgentDef(a_name, capacity)
if not auto_agents:
for i in range(agents_count):
a_name = 'a' + str(i)
agents[a_name] = AgentDef(a_name, capacity)
elif arity == 2:
edges_count = int(variable_count * (variable_count - 1) * density / 2)
if constraint_count != edges_count:
logger.warning("edges count is different of constraint count ({} "
"!= {}) but for arity 2, constraints are the deges"
"of the graph. We use the density ({}) to determine"
" the number of edges".format(
edges_count, constraint_count, density))
is_connected = False
while not is_connected:
graph = nx.gnp_random_graph(variable_count, density)
is_connected = nx.is_connected(graph)
# Compute nb of hard constraints regarding the true density
real_density = nx.density(graph)
hard_count = args.hard_constraint * real_density * args.variable_count\
* (args.variable_count + 1) / 2
for i, node in enumerate(graph.nodes_iter()):
logger.debug('node %s - %s', node, i)
name = 'v' + str(i)
variables[name] = Variable(name, d)
if auto_agents:
a_name = 'a' + str(i)
agents[a_name] = AgentDef(a_name, capacity)
if not auto_agents:
for i in range(agents_count):
a_name = 'a' + str(i)
agents[a_name] = AgentDef(a_name, capacity)
constraints = {}
for i, edge in enumerate(graph.edges_iter()):
logger.debug('edge %s - %s', edge, i)
name = 'c' + str(i)
u, v = edge
weights = [round(choose_weight(), 2), round(choose_weight(), 2)]
# Create hard_constraints
if i < hard_count:
objective = round(find_objective(weights, domain_range, True),
2)
expression = "0 if v{} != v{} else float(" \
"'inf')".format(u, v)
else:
max_val = (weights[0] + weights[1]) * domain_range
expression = 'abs(v{} + v{} - {})'\
.format(u, v, round(random.uniform(0, max_val), 2))
constraints[name] = relation_from_str(name, expression,
variables.values())
logger.debug(repr(constraints[name]))
else:
if edges_count < constraint_count and arity != 1:
raise ValueError(
"The number of edges must be greater or equal to the "
"number of constraints. Otherwise you have unused "
"constraints. Edges: {}, Constraints: {}".format(
edges_count, constraint_count))
nodes = [i for i in list(range(variable_count))]
constraints = [("c{}".format(i), "hard") if i < hard_count else
("c{}".format(i), "soft")
for i in list(range(constraint_count))]
# Randomly add edges
edges = defaultdict(lambda: []) # final set of edges
# Available edges at a given run
available_edges = {n: constraints.copy() for n in nodes}
# First, make sure each variable has one constraint
for n in nodes:
if constraints:
node = n
c = constraints.pop(random.randint(0, len(constraints) - 1))
else:
node, c = choose_in_available_edges(available_edges, n)
add_edge(node, c, available_edges, edges, arity)
logger.debug('Add edge (%s, %s)', n, c)
edges_count -= variable_count
# Second, make sure each constraint is used
for c in constraints:
n = random.choice(nodes)
add_edge(n, c, available_edges, edges, arity)
edges_count -= 1
logger.debug('Add edge (%s, %s)', n, c)
# Third, randomly add remaining constraints
while edges_count != 0:
n, c = choose_in_available_edges(available_edges)
if (n, c) == (None, None):
# If more edges than possible are asked, returns just the maximum
# edges (regarding nodes number and constraints arity)
logger.warning(
'%s edges were not added because you asked for too'
' many edges regarding the number of constraints ('
'%s) and their maximum arity (%s)',
edges_count - len(edges), constraint_count, args.arity)
break
else:
add_edge(n, c, available_edges, edges, arity)
edges_count -= 1
# Now create a DCOP from the graph
for i in nodes:
name = 'v' + str(i)
variables[name] = Variable(name, d)
if auto_agents:
a_name = 'a' + str(i)
agents[a_name] = AgentDef(a_name, capacity)
if not auto_agents:
for i in range(agents_count):
a_name = 'a' + str(i)
agents[a_name] = AgentDef(a_name, capacity)
constraints = {}
for c, neighbors in edges.items():
logger.debug('Constraint %s, variables: %s', c, neighbors)
name, is_hard = c[0], c[1] == 'hard'
c_variables = [variables["v" + str(name)] for name in neighbors]
addition_string = ""
first = True
weights = []
max_val = 0
for i in neighbors:
# Add random weights in constraints
m = round(choose_weight(), 2)
weights.append(m)
max_val += m * (domain_range - 1)
if not first:
addition_string += ' + '
else:
first = False
if m != 1:
addition_string += str(m) + '*'
addition_string += 'v' + str(i) + ' '
# To ensure that our hard constraints are achievable, we use
# the value obtained for a set of random values (in the domain)
# as the objective.
objective = round(find_objective(weights, domain_range, is_hard),
2)
if is_hard:
expression = "0 if " + addition_string + " == " + \
str(objective) + " else float('inf')"
else:
const_function = "abs(" + addition_string
if objective:
expression = const_function + " - " + str(objective) + ")"
else:
expression = addition_string
constraints[name] = relation_from_str(name, expression,
c_variables)
logger.debug(repr(constraints[name]))
dcop = DCOP('mixed constraints problem',
'min',
domains={'levels': d},
variables=variables,
constraints=constraints,
agents=agents)
if args.output is not None:
outputfile = args.output[0]
if args.correct_density:
outputfile = correct_density(outputfile, real_density)
write_in_file(outputfile, dcop_yaml(dcop))
else:
print(dcop_yaml(dcop))
def generate_graph_coloring(args):
logger.debug('generate_graph_coloring %s ', args)
node_count = args.node_count
density = args.density
color_count = args.color_count
auto_agents = args.agents is None
agents_count = node_count if auto_agents else args.agents
capacity = args.capacity
logger.info(
'Generating random graph coloring with %s variables, '
'%s colors, target density %s and %s agents', node_count, color_count,
args.density, agents_count)
# First a random graph
is_connected = False
if not args.allow_subgraph:
while not is_connected:
graph = nx.gnp_random_graph(args.node_count, density)
is_connected = nx.is_connected(graph)
else:
graph = nx.gnp_random_graph(args.node_count, density)
is_connected = nx.is_connected(graph)
real_density = nx.density(graph)
logger.info(nx.info(graph))
logger.info('Connected : %s', nx.is_connected(graph))
logger.info('Density %s', nx.density(graph))
# Now create a DCOP from the graph
d = VariableDomain('colors', 'color', range(color_count))
variables = {}
agents = {}
for i, node in enumerate(graph.nodes_iter()):
logger.debug('node %s - %s', node, i)
name = 'v' + str(i)
variables[name] = Variable(name, d)
if auto_agents:
a_name = 'a' + str(i)
agents[a_name] = AgentDef(a_name, capacity)
if not auto_agents:
for i in range(agents_count):
a_name = 'a' + str(i)
agents[a_name] = AgentDef(a_name, capacity)
constraints = {}
for i, edge in enumerate(graph.edges_iter()):
logger.debug('edge %s - %s', edge, i)
name = 'c' + str(i)
u, v = edge
expression = '1000 if v{} == v{} else 0'.format(u, v)
constraints[name] = relation_from_str(name, expression,
variables.values())
logger.debug(repr(constraints[name]))
dcop = DCOP('graph coloring',
'min',
domains={'colors': d},
variables=variables,
agents=agents,
constraints=constraints)
if args.output:
outputfile = args.output[0]
if args.correct_density:
outputfile = correct_density(outputfile, real_density)
write_in_file(outputfile, dcop_yaml(dcop))
else:
print(dcop_yaml(dcop))
def generate_iot(args):
print("generate iot ", args.output)
# Constraints and variables with a power-law constraint graph:
variables, constraints, domain = generate_powerlaw_var_constraints(
args.num, args.domain, args.range
)
# Build a dcop and computation graph with no agents, just to be able to
# compute the footprint of computations:
dcop = DCOP(
"graph coloring",
"min",
domains={"d": domain},
variables=variables,
agents={},
constraints=constraints,
)
graph_module = import_module("pydcop.computations_graph.factor_graph")
cg = graph_module.build_computation_graph(dcop)
algo_module = load_algorithm_module("maxsum")
footprints = {c.name: algo_module.computation_memory(c) for c in cg.nodes}
# Generate an agent for each variable computation and assign the
# computation to that agent.
agents = {} # type: Dict[str, AgentDef]
mapping = defaultdict(lambda: []) # type: Dict[str, List[str]]
for comp in cg.nodes:
if isinstance(comp, VariableComputationNode):
a_name = agt_name(comp.name)
agt = AgentDef(
a_name,
capacity=footprints[comp.name] * 100,
default_hosting_cost=10,
hosting_costs=agt_hosting_costs(comp, cg),
default_route=1,
routes=agt_route_costs(comp, cg),
)
logger.debug(
"Create agent %s for computation %s with capacity %s",
agt.name,
comp.name,
agt.capacity,
)
agents[agt.name] = agt
mapping[agt.name].append(comp.name)
# Now, we have created all the agents and distributed all the variables
# let's distribute the factor computations.
msg_load = msg_load_func(cg, algo_module.communication_load)
factor_mapping = distribute_factors(agents, cg, footprints, mapping, msg_load)
for a in mapping:
mapping[a].extend(factor_mapping[a])
dcop = DCOP(
"graph coloring",
"min",
domains={"d": domain},
variables=variables,
agents=agents,
constraints=constraints,
)
distribution = Distribution(mapping)
if args.output:
outputfile = args.output
write_in_file(outputfile, dcop_yaml(dcop))
dist = distribution.mapping()
cost = ilp_compref.distribution_cost(
distribution,
cg,
dcop.agents.values(),
computation_memory=algo_module.computation_memory,
communication_load=algo_module.communication_load,
)
result = {
"inputs": {
"dist_algo": "io_problem",
"dcop": args.output,
"graph": "factor_graph",
"algo": "maxsum",
},
"distribution": dist,
"cost": cost,
}
outputfile = "dist_" + args.output
write_in_file(outputfile, yaml.dump(result))
else:
print(dcop_yaml(dcop))
def generate(args):
slots, events, resources = generate_problem_definition(
args.slots_count,
args.resources_count,
args.max_resource_value,
args.events_count,
args.max_length_event,
args.max_resources_event,
)
penalty = args.max_resource_value * args.slots_count * args.resources_count
variables, constraints, agents = peav_model(slots, events, resources,
penalty)
domains = {
variable.domain.name: variable.domain
for variable in variables.values()
}
variables = {variable.name: variable for variable in variables.values()}
# agents_defs = {agent.name: agent for agent, _ in agents.values()}
# Generate agents hosting and route costs
agents_defs = {}
if not args.no_agents:
for agent, agt_variables in agents.items():
kw = {}
kw["hosting_costs"] = {v.name: 0 for v in agt_variables}
if args.hosting_default:
kw["default_hosting_cost"] = args.hosting_default
if args.capacity:
kw["capacity"] = args.capacity
if args.routes_default:
kw["default_route"] = args.routes_default
agents_defs[agent] = AgentDef(agent, **kw)
dcop = DCOP(
"MeetingSceduling",
objective="max",
domains=domains,
variables=variables,
constraints=constraints,
agents=agents_defs,
)
if not args.no_agents:
distribution = Distribution({
agent.name: [v.name for v in agents[agent.name]]
for agent in agents_defs.values()
})
if args.output:
output_file = args.output
with open(output_file, encoding="utf-8", mode="w") as fo:
fo.write(dcop_yaml(dcop))
if not args.no_agents:
dist_result = {
"inputs": {
"dist_algo": "peav",
"dcop": output_file,
"graph": "constraints_graph",
"algo": "NA",
},
"distribution": distribution.mapping(),
"cost": None,
}
path, ext = splitext(output_file)
dist_output_file = f"{path}_dist{ext}"
with open(dist_output_file, encoding="utf-8", mode="w") as fo:
fo.write(yaml.dump(dist_result))
else:
print(dcop_yaml(dcop))
if not args.no_agents:
dist_result = {
"inputs": {
"dist_algo": "peav",
"dcop": "NA",
"graph": "constraints_graph",
"algo": "NA",
},
"distribution": distribution.mapping(),
"cost": None,
}
# FIXME proper serialization of the distribution:
print(yaml.dump(dist_result))
def generate_small_world(args):
logger.debug("generate small world problem %s ", args)
# Erdős-Rényi graph aka binomial graph.
graph = nx.barabasi_albert_graph(args.num, 2)
# import matplotlib.pyplot as plt
# plt.subplot(121)
# nx.draw(graph) # default spring_layout
# plt.show()
domain = Domain("d", "d", range(args.domain))
variables = {}
agents = {}
for n in graph.nodes:
v = Variable(var_name(n), domain)
variables[v.name] = v
logger.debug("Create var for node %s : %s", n, v)
constraints = {}
for i, (n1, n2) in enumerate(graph.edges):
v1 = variables[var_name(n1)]
v2 = variables[var_name(n2)]
values = random_assignment_matrix([v1, v2], range(args.range))
c = NAryMatrixRelation([v1, v2], values, name=c_name(n1, n2))
logger.debug("Create constraints for edge (%s, %s) : %s", v1, v2, c)
constraints[c.name] = c
dcop = DCOP(
"graph coloring",
"min",
domains={"d": domain},
variables=variables,
agents={},
constraints=constraints,
)
graph_module = import_module("pydcop.computations_graph.factor_graph")
cg = graph_module.build_computation_graph(dcop)
algo_module = load_algorithm_module("maxsum")
footprints = {n.name: algo_module.computation_memory(n) for n in cg.nodes}
f_vals = footprints.values()
logger.info(
"%s computations, footprint: \n sum: %s, avg: %s max: %s, "
"min: %s",
len(footprints),
sum(f_vals),
sum(f_vals) / len(footprints),
max(f_vals),
min(f_vals),
)
default_hosting_cost = 2000
small_agents = [agt_name(i) for i in range(75)]
small_capa, avg_capa, big_capa = 40, 200, 1000
avg_agents = [agt_name(i) for i in range(75, 95)]
big_agents = [agt_name(i) for i in range(95, 100)]
hosting_factor = 10
for a in small_agents:
# communication costs with all other agents
comm_costs = {other: 6 for other in small_agents if other != a}
comm_costs.update({other: 8 for other in avg_agents})
comm_costs.update({other: 10 for other in big_agents})
# hosting cost for all computations
hosting_costs = {}
for n in cg.nodes:
# hosting_costs[n.name] = hosting_factor * \
# abs(small_capa -footprints[n.name])
hosting_costs[n.name] = footprints[n.name] / small_capa
agt = AgentDef(
a,
default_hosting_cost=default_hosting_cost,
hosting_costs=hosting_costs,
default_route=10,
routes=comm_costs,
capacity=small_capa,
)
agents[agt.name] = agt
logger.debug("Create small agt : %s", agt)
for a in avg_agents:
# communication costs with all other agents
comm_costs = {other: 8 for other in small_agents}
comm_costs.update({other: 2 for other in avg_agents if other != a})
comm_costs.update({other: 4 for other in big_agents})
# hosting cost for all computations
hosting_costs = {}
for n in cg.nodes:
# hosting_costs[n.name] = hosting_factor * \
# abs(avg_capa - footprints[n.name])
hosting_costs[n.name] = footprints[n.name] / avg_capa
agt = AgentDef(
a,
default_hosting_cost=default_hosting_cost,
hosting_costs=hosting_costs,
default_route=10,
routes=comm_costs,
capacity=avg_capa,
)
agents[agt.name] = agt
logger.debug("Create avg agt : %s", agt)
for a in big_agents:
# communication costs with all other agents
comm_costs = {other: 10 for other in small_agents}
comm_costs.update({other: 4 for other in avg_agents})
comm_costs.update({other: 1 for other in big_agents if other != a})
# hosting cost for all computations
hosting_costs = {}
for n in cg.nodes:
hosting_costs[n.name] = footprints[n.name] / big_capa
agt = AgentDef(
a,
default_hosting_cost=default_hosting_cost,
hosting_costs=hosting_costs,
default_route=10,
routes=comm_costs,
capacity=big_capa,
)
agents[agt.name] = agt
logger.debug("Create big agt : %s", agt)
dcop = DCOP(
"graph coloring",
"min",
domains={"d": domain},
variables=variables,
agents=agents,
constraints=constraints,
)
if args.output:
outputfile = args.output[0]
write_in_file(outputfile, dcop_yaml(dcop))
else:
print(dcop_yaml(dcop))