fixup! Prepare for pytorch tests

kiudee · Apr 8, 2021 · 2d5d425 · 2d5d425
1 parent 070a56e
commit 2d5d425
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diff --git a/csrank/tests/test_discrete_choice.py b/csrank/tests/test_discrete_choice.py
@@ -1,6 +1,7 @@
 import numpy as np
 from pymc3.variational.callbacks import CheckParametersConvergence
 import pytest
+import torch
 from torch import optim
 
 from csrank.constants import FATE_DC
@@ -80,6 +81,10 @@ def trivial_discrete_choice_problem():
 @pytest.mark.parametrize("name", list(discrete_choice_functions.keys()))
 def test_discrete_choice_function_fixed(trivial_discrete_choice_problem, name):
     np.random.seed(123)
+    # There are some caveats with pytorch reproducibility. See the comment on
+    # the corresponding line of `test_choice_functions.py` for details.
+    torch.manual_seed(123)
+    torch.use_deterministic_algorithms(True)
     x, y = trivial_discrete_choice_problem
     choice_function = discrete_choice_functions[name][0]
     params, accuracies = (

diff --git a/csrank/tests/test_ranking.py b/csrank/tests/test_ranking.py
@@ -1,5 +1,6 @@
 import numpy as np
 import pytest
+import torch
 from torch import optim
 
 from csrank.constants import ERR
@@ -52,6 +53,10 @@ def trivial_ranking_problem():
 @pytest.mark.parametrize("ranker_name", list(object_rankers.keys()))
 def test_object_ranker_fixed(trivial_ranking_problem, ranker_name):
     np.random.seed(123)
+    # There are some caveats with pytorch reproducibility. See the comment on
+    # the corresponding line of `test_choice_functions.py` for details.
+    torch.manual_seed(123)
+    torch.use_deterministic_algorithms(True)
     x, y = trivial_ranking_problem
     ranker, params, (loss, acc) = object_rankers[ranker_name]
     ranker = ranker(**params)