Optimizer.compute_optimal_parameters should return mutable list

open-spaced-repetition · Jan 24, 2025 · 911be9a · 911be9a
1 parent 153862e
commit 911be9a
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Showing 2 changed files with 4 additions and 4 deletions.
diff --git a/fsrs/optimizer.py b/fsrs/optimizer.py
@@ -274,7 +274,7 @@ def _update_parameters(
             num_reviews = _num_reviews()
 
             if num_reviews < mini_batch_size:
-                return DEFAULT_PARAMETERS
+                return list(DEFAULT_PARAMETERS)
 
             # Define FSRS Scheduler parameters as torch tensors with gradients
             params = torch.tensor(

diff --git a/tests/test_optimizer.py b/tests/test_optimizer.py
@@ -45,7 +45,7 @@ def test_zero_revlogs(self):
 
         optimal_parameters = optimizer.compute_optimal_parameters()
 
-        assert optimal_parameters == DEFAULT_PARAMETERS
+        assert optimal_parameters == list(DEFAULT_PARAMETERS)
 
     def test_review_logs(self):
         """
@@ -81,7 +81,7 @@ def test_review_logs(self):
         optimal_parameters = optimizer.compute_optimal_parameters()
 
         # the optimal paramaters are no longer equal to the starting parameters
-        assert optimal_parameters != DEFAULT_PARAMETERS
+        assert optimal_parameters != list(DEFAULT_PARAMETERS)
 
         # the output is expected
         assert np.allclose(optimal_parameters, expected_optimal_parameters)
@@ -114,7 +114,7 @@ def test_few_review_logs(self):
 
         optimal_parameters = optimizer.compute_optimal_parameters()
 
-        assert optimal_parameters == DEFAULT_PARAMETERS
+        assert optimal_parameters == list(DEFAULT_PARAMETERS)
 
     def test_unordered_review_logs(self):
         """