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| author | Marc Glisse <marc.glisse@inria.fr> | 2020-05-05 11:07:53 +0200 |
|---|---|---|
| committer | Marc Glisse <marc.glisse@inria.fr> | 2020-05-05 11:07:53 +0200 |
| commit | 99549c20e9173b536ac816ab683bc13025f182a2 (patch) | |
| tree | 533788ca5487a08390215c5c2424d61f991595af | |
| parent | 81a4e6ff3ba732bd4e061fc5443ffed52b694e01 (diff) | |
fix use of threads and n_jobs in Parallel
| -rw-r--r-- | src/python/gudhi/point_cloud/knn.py | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/src/python/gudhi/point_cloud/knn.py b/src/python/gudhi/point_cloud/knn.py index 07553d6d..34e80b5d 100644 --- a/src/python/gudhi/point_cloud/knn.py +++ b/src/python/gudhi/point_cloud/knn.py @@ -200,8 +200,8 @@ class KNearestNeighbors: from joblib import Parallel, delayed, effective_n_jobs from sklearn.utils import gen_even_slices - slices = gen_even_slices(len(X), effective_n_jobs(-1)) - parallel = Parallel(backend="threading", n_jobs=-1) + slices = gen_even_slices(len(X), effective_n_jobs(n_jobs)) + parallel = Parallel(prefer="threads", n_jobs=n_jobs) if self.params.get("sort_results", True): def func(M): @@ -242,8 +242,8 @@ class KNearestNeighbors: else: func = lambda M: numpy.partition(M, k - 1)[:, 0:k] - slices = gen_even_slices(len(X), effective_n_jobs(-1)) - parallel = Parallel(backend="threading", n_jobs=-1) + slices = gen_even_slices(len(X), effective_n_jobs(n_jobs)) + parallel = Parallel(prefer="threads", n_jobs=n_jobs) distances = numpy.concatenate(parallel(delayed(func)(X[s]) for s in slices)) return distances return None |