def _get_context(self):
currdir = os.path.dirname(os.path.realpath(__file__))
context = kahypar.Context()
context.loadINIconfiguration(currdir + "/kahypar_config.ini")
context.setK(k=2)
context.setEpsilon(self.epsilon)
context.suppressOutput(True)
return context
def __init__(self, tilesize=8, mnc=100, addMsgNets=True, config=None):
self._tilesize = tilesize
self._mnc = mnc
self._addMsgNets = addMsgNets
self._context = kahypar.Context()
with to_ini(config) as ini:
self._context.loadINIconfiguration(ini)
def hgpa(labels, nclass, random_state):
"""HyperGraph Partitioning Algorithm (HGPA).
Parameters
----------
labels: Labels generated by multiple clustering algorithms such as K-Means.
nclass: Number of classes in a consensus clustering label.
random_state: Used for reproducible results.
Return
-------
label_ce: Consensus clustering label obtained from HGPA.
"""
# Create hypergraph for kahypar
H = create_hypergraph(labels)
n_nodes, n_nets = H.shape
node_weights = [1] * n_nodes
edge_weights = [1] * n_nets
hyperedge_indices = [0]
hyperedges = []
HT = H.T
for i in range(n_nets):
h = HT.getrow(i)
idx_row, idx_col = h.nonzero()
hyperedges += list(idx_col)
hyperedge_indices.append(len(hyperedges))
hypergraph = kahypar.Hypergraph(n_nodes, n_nets, hyperedge_indices,
hyperedges, nclass, edge_weights,
node_weights)
# Settings for kahypar
context = kahypar.Context()
config_path = os.path.dirname(
__file__) + "/kahypar_config/km1_kKaHyPar_sea20.ini"
context.loadINIconfiguration(config_path)
if random_state is not None:
context.setSeed(random_state)
context.setK(nclass)
context.setEpsilon(0.03)
context.suppressOutput(True)
# Hypergraph partitioning
kahypar.partition(hypergraph, context)
label_ce = np.empty(n_nodes, dtype=int)
for i in range(n_nodes):
label_ce[i] = hypergraph.blockID(i)
return label_ce
def _set_context(self, **kwargs):
mode = modes[int(kwargs.get('mode'))]
objective = objectives[int(kwargs.get('objective'))]
K = int(kwargs.get('K'))
eps = kwargs.get('eps')
seed = kwargs.get('seed')
profile_mode = {'direct': 'k', 'recursive': 'r'}[mode]
profile = f"{objective}_{profile_mode}KaHyPar_sea20.ini"
context = kahypar.Context()
context.loadINIconfiguration(join(KAHYPAR_PROFILE_DIR, profile))
context.setK(K)
context.setSeed(seed)
context.setEpsilon(kwargs.get('epsilon', eps * (K - 1)))
context.suppressOutput(kwargs.get('quiet', True))
return context
def test_partition_hypergraph(self):
context = kahypar.Context()
context.loadINIconfiguration(
mydir + "/../..//config/km1_kKaHyPar_dissertation.ini")
ibm01 = kahypar.createHypergraphFromFile(mydir + "/ISPD98_ibm01.hgr",
2)
context.setK(2)
context.setEpsilon(0.03)
kahypar.partition(ibm01, context)
self.assertEqual(kahypar.cut(ibm01), 202)
self.assertEqual(kahypar.soed(ibm01), 404)
self.assertEqual(kahypar.connectivityMinusOne(ibm01), 202)
self.assertEqual(kahypar.imbalance(ibm01, context),
0.027603513174403904)
def kahypar_subgraph_find_membership(
inputs,
output,
size_dict,
weight_nodes='const',
weight_edges='log',
fix_output_nodes=False,
parts=2,
imbalance=0.01,
compress=0,
seed=None,
profile=None,
mode='direct',
objective='cut',
quiet=True,
):
import kahypar as kahypar
if seed is None:
seed = random.randint(0, 2**31 - 1)
nv = len(inputs)
if parts >= nv:
return list(range(nv))
hg = get_hypergraph(inputs, output, size_dict, accel=False)
if compress:
hg.compress(compress)
winfo = to_sparse(hg, weight_nodes=weight_nodes, weight_edges=weight_edges)
hypergraph_kwargs = {
'num_nodes': hg.get_num_nodes(),
'num_edges': hg.get_num_edges(),
'index_vector': winfo['hyperedge_indices'],
'edge_vector': winfo['hyperedges'],
'k': parts,
}
edge_weights, node_weights = {
(False, False): (None, None),
(False, True): ([], winfo['node_weights']),
(True, False): (winfo['edge_weights'], []),
(True, True): (winfo['edge_weights'], winfo['node_weights']),
}[winfo['has_edge_weights'], winfo['has_node_weights']]
if edge_weights or node_weights:
hypergraph_kwargs['edge_weights'] = edge_weights
hypergraph_kwargs['node_weights'] = node_weights
hypergraph = kahypar.Hypergraph(**hypergraph_kwargs)
if fix_output_nodes:
# make sure all the output nodes (those with output indices) are in
# the same partition
onodes = tuple(hg.output_nodes())
if parts >= nv - len(onodes) + 1:
# too many partitions, simply group all outputs and return
groups = itertools.count(1)
return [0 if i in onodes else next(groups) for i in range(nv)]
for i in onodes:
hypergraph.fixNodeToBlock(i, 0)
# silences various warnings
mode = 'recursive'
if profile is None:
profile_mode = {'direct': 'k', 'recursive': 'r'}[mode]
profile = f"{objective}_{profile_mode}KaHyPar_sea20.ini"
context = kahypar.Context()
context.loadINIconfiguration(join(KAHYPAR_PROFILE_DIR, profile))
context.setK(parts)
context.setSeed(seed)
context.suppressOutput(quiet)
context.setEpsilon(imbalance * parts)
kahypar.partition(hypergraph, context)
return [hypergraph.blockID(i) for i in hypergraph.nodes()]
import os
import kahypar as kahypar
num_nodes = 7
num_nets = 4
hyperedge_indices = [0,2,6,9,12]
hyperedges = [0,2,0,1,3,4,3,4,6,2,5,6]
node_weights = [1,2,3,4,5,6,7]
edge_weights = [11,22,33,44]
k=2
hypergraph = kahypar.Hypergraph(num_nodes, num_nets, hyperedge_indices, hyperedges, k, edge_weights, node_weights)
context = kahypar.Context()
context.loadINIconfiguration("/home/ags/code/kahypar/config/km1_kKaHyPar_sea20.ini")
context.setK(k)
context.setEpsilon(0.03)
kahypar.partition(hypergraph, context)
def kahypar_subgraph_find_membership(
inputs,
output,
size_dict,
weight_nodes='constant',
weight_edges='log',
fuse_output_inds=False,
parts=2,
imbalance=0.01,
seed=None,
profile=None,
mode='direct',
objective='cut',
quiet=True,
):
import kahypar as kahypar
if seed is None:
seed = random.randint(0, 2**31 - 1)
nv = len(inputs)
if parts >= nv:
return list(range(nv))
HG = HyperGraph(inputs, output, size_dict,
weight_edges=weight_edges,
weight_nodes=weight_nodes,
fuse_output_inds=fuse_output_inds)
hyperedge_indices, hyperedges = HG.to_sparse()
hypergraph_kwargs = {
'num_nodes': HG.num_vertices,
'num_edges': HG.num_edges,
'index_vector': hyperedge_indices,
'edge_vector': hyperedges,
'k': parts,
}
edge_weights, node_weights = {
(False, False): (None, None),
(False, True): ([], HG.node_weights),
(True, False): (HG.edge_weights, []),
(True, True): (HG.edge_weights, HG.node_weights),
}[HG.has_edge_weights, HG.has_node_weights]
if edge_weights or node_weights:
hypergraph_kwargs['edge_weights'] = edge_weights
hypergraph_kwargs['node_weights'] = node_weights
hypergraph = kahypar.Hypergraph(**hypergraph_kwargs)
if profile is None:
profile_mode = {'direct': 'k', 'recursive': 'r'}[mode]
profile = f"{objective}_{profile_mode}KaHyPar_dissertation.ini"
context = kahypar.Context()
context.loadINIconfiguration(join(KAHYPAR_PROFILE_DIR, profile))
context.setK(parts)
context.setSeed(seed)
context.suppressOutput(quiet)
context.setEpsilon(imbalance * parts)
kahypar.partition(hypergraph, context)
return [hypergraph.blockID(i) for i in hypergraph.nodes()]
