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[ENH] added joblib parallelization to consensus_modularity #150

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[ENH] added joblib parallelization to consensus_modularity #150

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14 changes: 10 additions & 4 deletions netneurotools/modularity/modules.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
from sklearn.utils.validation import check_random_state
from scipy import optimize
from scipy.cluster import hierarchy
from joblib import Parallel, delayed

from .. import has_numba
if has_numba:
Expand Down Expand Up @@ -368,7 +369,8 @@ def find_consensus(assignments, null_func=np.mean, return_agreement=False,


def consensus_modularity(adjacency, gamma=1, B='modularity',
repeats=250, null_func=np.mean, seed=None):
repeats=250, null_func=np.mean, seed=None,
n_jobs=1):
"""
Find community assignments from `adjacency` through consensus.

Expand All @@ -394,6 +396,8 @@ def consensus_modularity(adjacency, gamma=1, B='modularity',
Default: `np.mean`
seed : {int, np.random.RandomState instance, None}, optional
Seed for random number generation. Default: None
n_jobs : int, optional
Number of parallel jobs to run. Default: 1

Returns
-------
Expand All @@ -411,9 +415,11 @@ def consensus_modularity(adjacency, gamma=1, B='modularity',
structure in networks. Chaos: An Interdisciplinary Journal of Nonlinear
Science, 23(1), 013142.
"""
# generate community partitions `repeat` times
comms, Q_all = zip(*[bct.community_louvain(adjacency, gamma=gamma, B=B)
for i in range(repeats)])
# generate community partitions `repeat` times with parallelization
comms, Q_all = zip(*Parallel(n_jobs=n_jobs)(
delayed(bct.community_louvain)(adjacency, gamma=gamma, B=B,
seed=seed)
for _ in range(repeats)))
comms = np.column_stack(comms)

# find consensus cluster assignments across all partitoning solutions
Expand Down