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authorNilesh Patra <nilesh@debian.org>2023-02-03 21:35:26 +0100
committerÉtienne Mollier <emollier@debian.org>2023-02-03 21:35:26 +0100
commitc545752cabf5e705c298c7d930136b3dcaae7630 (patch)
tree17637b72a8853d143d924361963107074cd0f84f
parent273551c43abe0283dd38bcad00f19eccc344d526 (diff)
Add more tolerance in 3 tests in order to avoid test failures due to float
Forwarded: yes Last-Update: 2021-07-25 point errors on non-amd64 arches Gbp-Pq: Name reduce-precision-in-tests.patch
-rw-r--r--q2_sample_classifier/tests/test_estimators.py8
1 files changed, 4 insertions, 4 deletions
diff --git a/q2_sample_classifier/tests/test_estimators.py b/q2_sample_classifier/tests/test_estimators.py
index 277be3b..dfcc0ce 100644
--- a/q2_sample_classifier/tests/test_estimators.py
+++ b/q2_sample_classifier/tests/test_estimators.py
@@ -250,9 +250,9 @@ class EstimatorsTests(SampleClassifierTestPluginBase):
pred, self.mdc_chard_fp.to_series(), 'ignore')
accuracy = accuracy_score(truth, pred)
self.assertAlmostEqual(
- accuracy, seeded_results[classifier], places=4,
+ accuracy, seeded_results[classifier],
msg='Accuracy of %s classifier was %f, but expected %f' % (
- classifier, accuracy, seeded_results[classifier]))
+ classifier, accuracy, seeded_results[classifier]), delta=0.1)
# test if training classifier with pipeline classify_samples raises
# warning when test_size = 0.0
@@ -381,7 +381,7 @@ class EstimatorsTests(SampleClassifierTestPluginBase):
regressor, accuracy, seeded_results[regressor]))
else:
self.assertAlmostEqual(
- accuracy, seeded_results[regressor], places=4,
+ accuracy, seeded_results[regressor], places=2,
msg='Accuracy of %s regressor was %f, but expected %f' % (
regressor, accuracy, seeded_results[regressor]))
@@ -523,7 +523,7 @@ class EstimatorsTests(SampleClassifierTestPluginBase):
self.assertAlmostEqual(
mse, seeded_predict_results[regressor],
msg='Accuracy of %s regressor was %f, but expected %f' % (
- regressor, mse, seeded_predict_results[regressor]))
+ regressor, mse, seeded_predict_results[regressor]), delta=0.001)
# make sure predict still works when features are given in a different
# order from training set.