diff options
author | Hind-M <hind.montassif@gmail.com> | 2021-10-07 15:25:25 +0200 |
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committer | Hind-M <hind.montassif@gmail.com> | 2021-10-07 15:25:25 +0200 |
commit | dbdc62a494e54c3dd409a2e80fa169560355ce19 (patch) | |
tree | f09ebd703694a5d964f271bd286383627d618f7f /src/python/gudhi | |
parent | 145fcba2de5f174b8fcdeab5ac1997978ffcdc0d (diff) |
Move warnings import to the beginning of knn.py file
Use isfinite instead of isinf and isnan
Use catch_warnings context manager instead of "always" with simplefilter
Diffstat (limited to 'src/python/gudhi')
-rw-r--r-- | src/python/gudhi/point_cloud/knn.py | 34 |
1 files changed, 10 insertions, 24 deletions
diff --git a/src/python/gudhi/point_cloud/knn.py b/src/python/gudhi/point_cloud/knn.py index 0724ce94..de5844f9 100644 --- a/src/python/gudhi/point_cloud/knn.py +++ b/src/python/gudhi/point_cloud/knn.py @@ -8,6 +8,7 @@ # - YYYY/MM Author: Description of the modification import numpy +import warnings # TODO: https://github.com/facebookresearch/faiss @@ -257,14 +258,9 @@ class KNearestNeighbors: if ef is not None: self.graph.set_ef(ef) neighbors, distances = self.graph.knn_query(X, k, num_threads=self.params["num_threads"]) - if numpy.any(numpy.isnan(distances)): - import warnings - warnings.simplefilter("always") - warnings.warn("NaN value encountered while computing 'distances'", RuntimeWarning) - if numpy.any(numpy.isinf(distances)): - import warnings - warnings.simplefilter("always") - warnings.warn("Overflow value encountered while computing 'distances'", RuntimeWarning) + with warnings.catch_warnings(): + if not(numpy.all(numpy.isfinite(distances))): + warnings.warn("Overflow/infinite value encountered while computing 'distances'", RuntimeWarning) # The k nearest neighbors are always sorted. I couldn't find it in the doc, but the code calls searchKnn, # which returns a priority_queue, and then fills the return array backwards with top/pop on the queue. if self.return_index: @@ -298,14 +294,9 @@ class KNearestNeighbors: if self.return_index: if self.return_distance: distances, neighbors = mat.Kmin_argKmin(k, dim=1) - if torch.isnan(distances).any(): - import warnings - warnings.simplefilter("always") - warnings.warn("NaN value encountered while computing 'distances'", RuntimeWarning) - if torch.isinf(distances).any(): - import warnings - warnings.simplefilter("always") - warnings.warn("Overflow encountered while computing 'distances'", RuntimeWarning) + with warnings.catch_warnings(): + if not(torch.isfinite(distances).all()): + warnings.warn("Overflow/infinite value encountered while computing 'distances'", RuntimeWarning) if p != numpy.inf: distances = distances ** (1.0 / p) return neighbors, distances @@ -314,14 +305,9 @@ class KNearestNeighbors: return neighbors if self.return_distance: distances = mat.Kmin(k, dim=1) - if torch.isnan(distances).any(): - import warnings - warnings.simplefilter("always") - warnings.warn("NaN value encountered while computing 'distances'", RuntimeWarning) - if torch.isinf(distances).any(): - import warnings - warnings.simplefilter("always") - warnings.warn("Overflow encountered while computing 'distances'", RuntimeWarning) + with warnings.catch_warnings(): + if not(torch.isfinite(distances).all()): + warnings.warn("Overflow/infinite value encountered while computing 'distances'", RuntimeWarning) if p != numpy.inf: distances = distances ** (1.0 / p) return distances |