From 145fcba2de5f174b8fcdeab5ac1997978ffcdc0d Mon Sep 17 00:00:00 2001
From: Hind-M <hind.montassif@gmail.com>
Date: Wed, 8 Sep 2021 18:01:11 +0200
Subject: Set the warning filter to "always" Add test for dtm overflow warning

---
 src/python/gudhi/point_cloud/knn.py |  6 ++++++
 src/python/test/test_dtm.py         | 14 ++++++++++++++
 2 files changed, 20 insertions(+)

(limited to 'src')

diff --git a/src/python/gudhi/point_cloud/knn.py b/src/python/gudhi/point_cloud/knn.py
index dec5f88f..0724ce94 100644
--- a/src/python/gudhi/point_cloud/knn.py
+++ b/src/python/gudhi/point_cloud/knn.py
@@ -259,9 +259,11 @@ class KNearestNeighbors:
             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)
             # 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.
@@ -298,9 +300,11 @@ class KNearestNeighbors:
                     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)
                     if p != numpy.inf:
                         distances = distances ** (1.0 / p)
@@ -312,9 +316,11 @@ class KNearestNeighbors:
                 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)
                 if p != numpy.inf:
                     distances = distances ** (1.0 / p)
diff --git a/src/python/test/test_dtm.py b/src/python/test/test_dtm.py
index 0a52279e..c29471cf 100755
--- a/src/python/test/test_dtm.py
+++ b/src/python/test/test_dtm.py
@@ -13,6 +13,7 @@ import numpy
 import pytest
 import torch
 import math
+import warnings
 
 
 def test_dtm_compare_euclidean():
@@ -87,3 +88,16 @@ def test_density():
     assert density == pytest.approx(expected)
     density = DTMDensity(weights=[0.5, 0.5], metric="neighbors", dim=1).fit_transform(distances)
     assert density == pytest.approx(expected)
+
+def test_dtm_overflow_warnings():
+    pts = numpy.array([[10., 100000000000000000000000000000.], [1000., 100000000000000000000000000.]])
+    impl_warn = ["keops", "hnsw"]
+
+    with warnings.catch_warnings(record=True) as w:
+        for impl in impl_warn:
+            dtm = DistanceToMeasure(2, q=10000, implementation=impl)
+            r = dtm.fit_transform(pts)
+        assert len(w) == 2
+        for i in range(len(w)):
+            assert issubclass(w[i].category, RuntimeWarning)
+            assert "Overflow" in str(w[i].message)
-- 
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