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# Copyright 2017 Orange
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import unittest
from subprocess import check_output, STDOUT, CalledProcessError
import yaml
from tests.dcop_cli.utils import instance_path
class GraphColoring1(unittest.TestCase):
def test_oneagent_pseudotree(self):
result = run_distribute('graph_coloring1.yaml', 'oneagent',
'pseudotree')
dist = result['distribution']
# With oneagent distribution, each agent host at most one computation
for a in dist:
self.assertLessEqual(len(dist[a]), 1)
self.assertTrue(is_hosted(dist, 'v1'))
self.assertTrue(is_hosted(dist, 'v2'))
self.assertTrue(is_hosted(dist, 'v3'))
def test_oneagent_factorgraph(self):
result = run_distribute('graph_coloring1.yaml', 'oneagent',
'factor_graph')
dist = result['distribution']
# With oneagent distribution, each agent host at most one computation
for a in dist:
self.assertLessEqual(len(dist[a]), 1)
self.assertTrue(is_hosted(dist, 'v1'))
self.assertTrue(is_hosted(dist, 'v2'))
self.assertTrue(is_hosted(dist, 'v3'))
def test_oneagent_constraints_hypergraph(self):
result = run_distribute('graph_coloring1.yaml', 'oneagent',
'constraints_hypergraph')
dist = result['distribution']
# With oneagent distribution, each agent host at most one computation
for a in dist:
self.assertLessEqual(len(dist[a]), 1)
self.assertTrue(is_hosted(dist, 'v1'))
self.assertTrue(is_hosted(dist, 'v2'))
self.assertTrue(is_hosted(dist, 'v3'))
@unittest.skip('dpop does not define computation size')
def test_adhoc_pseudotree(self):
result = run_distribute('graph_coloring1.yaml', 'adhoc',
'pseudotree', algo='dpop')
dist = result['distribution']
self.assertTrue(is_hosted(dist, 'v1'))
self.assertTrue(is_hosted(dist, 'v2'))
self.assertTrue(is_hosted(dist, 'v3'))
def test_adhoc_factorgraph(self):
result = run_distribute('graph_coloring1.yaml', 'adhoc',
'factor_graph', algo='maxsum')
dist = result['distribution']
self.assertTrue(is_hosted(dist, 'v1'))
self.assertTrue(is_hosted(dist, 'v2'))
self.assertTrue(is_hosted(dist, 'v3'))
def test_adhoc_constraints_hypergraph(self):
result = run_distribute('graph_coloring1.yaml', 'adhoc',
'constraints_hypergraph', algo='dsa')
dist = result['distribution']
self.assertTrue(is_hosted(dist, 'v1'))
self.assertTrue(is_hosted(dist, 'v2'))
self.assertTrue(is_hosted(dist, 'v3'))
def test_ilp_fgdp_factorgraph(self):
# When using ilp-fgdp, we must also provide the algorithm
result = run_distribute('graph_coloring1.yaml', 'ilp_fgdp',
'factor_graph', algo='maxsum')
dist = result['distribution']
# ILP-FGDP requires that each agent hosts at least one computation
# and we have eaxctly 5 computations and 5 agents here:
for a in dist:
self.assertEqual(len(dist[a]), 1)
self.assertTrue(is_hosted(dist, 'v1'))
self.assertTrue(is_hosted(dist, 'v2'))
self.assertTrue(is_hosted(dist, 'v3'))
def test_ilp_fgdp_pseudotree(self):
# ilp-fgdp does not work with pseudo-tree graph model
self.assertRaises(CalledProcessError, run_distribute,
'graph_coloring1.yaml', 'ilp_fgdp',
'pseudotree', algo='maxsum')
def test_ilp_fgdp_constraints_hypergraph(self):
# ilp-fgdp does not work with pseudo-tree graph model
self.assertRaises(CalledProcessError, run_distribute,
'graph_coloring1.yaml', 'ilp_fgdp',
'constraints_hypergraph', algo='maxsum')
def test_ilp_compref_factorgraph(self):
# When using ilp-compref, we must also provide the algorithm
result = run_distribute('graph_coloring1.yaml', 'ilp_compref',
'factor_graph', algo='maxsum')
# lame: we do not check the result, we just ensure we do not crash
def test_ilp_compref_constraints_hypergraph(self):
result = run_distribute('graph_coloring1.yaml', 'ilp_compref',
'constraints_hypergraph', algo='dsa')
# lame: we do not check the result, we just ensure we do not crash
class DistAlgoOpionCompatibility(unittest.TestCase):
def test_dist_with_only_algo_only(self):
run_distribute('graph_coloring1.yaml', 'oneagent',
algo='maxsum')
def test_dist_with_graph_only(self):
run_distribute('graph_coloring1.yaml', 'oneagent',
graph='factor_graph')
def test_incompatible_graph_algo_must_fail(self):
self.assertRaises(CalledProcessError, run_distribute,
'graph_coloring1.yaml', 'oneagent',
algo='dsa', graph='factor_graph')
def is_hosted(mapping, computation: str):
for a in mapping:
if computation in mapping[a]:
return True
return False
def run_distribute(filename, distribution, graph=None, algo=None):
"""
Run the distribute cli command with the given parameters
"""
filename = instance_path(filename)
algo_opt = '' if algo is None else '-a ' + algo
graph_opt = '' if graph is None else '-g ' + graph
cmd = 'pydcop distribute -d {distribution} {graph_opt} ' \
'{algo_opt} {file}'.format(distribution=distribution,
graph_opt=graph_opt,
algo_opt=algo_opt,
file=filename)
output = check_output(cmd, stderr=STDOUT, timeout=10, shell=True)
return yaml.load(output.decode(encoding='utf-8'), Loader=yaml.FullLoader)
