Add compatibility fixes for pandas 3 and torch 2.10

.github/workflows/build.yml

@@ -27,7 +27,7 @@ jobs:
         # We aim to support the versions on pytorch.org
         # as well as selected previous versions on
         # https://pytorch.org/get-started/previous-versions/
-        torch-version: ["2.6.0", "2.9.1"]
+        torch-version: ["2.6.0", "2.10.0"]
         sklearn-version: ["latest"]
         include:
           # windows test with standard config

cebra/integrations/deeplabcut.py

@@ -169,7 +169,8 @@ def load_data(self, pcutoff: float = 0.6) -> npt.NDArray:
         pred_xy = []
         for i, _ in enumerate(self.dlc_df.index):
             data = (self.dlc_df.iloc[i].loc[self.scorer].loc[
-                self.keypoints_list].to_numpy().reshape(-1, len(dlc_df_coords)))
+                self.keypoints_list].to_numpy().copy().reshape(
+                    -1, len(dlc_df_coords)))
 
             # Handles nan values with interpolation
             if i > 0 and i < len(self.dlc_df) - 1:

cebra/integrations/sklearn/cebra.py

@@ -1253,6 +1253,8 @@ def transform(self,
 
         X = sklearn_utils.check_input_array(X, min_samples=len(self.offset_))
 
+        X = cebra_sklearn_dataset._ensure_writable(X)
+
         if isinstance(X, np.ndarray):
             X = torch.from_numpy(X)
 

cebra/integrations/sklearn/dataset.py

@@ -21,6 +21,8 @@
 #
 """Datasets to be used as part of the sklearn framework."""
 
+import traceback
+import warnings
 from typing import Iterable, Optional
 
 import numpy as np
@@ -34,6 +36,28 @@
 import cebra.solver
 
 
+def _ensure_writable(array: npt.NDArray) -> npt.NDArray:
+    if not array.flags.writeable:
+        stack = traceback.extract_stack()[-5:-1]
+        stack_str = ''.join(traceback.format_list(stack[-4:]))
+
+        warnings.warn(
+            ("You passed a non-writable Numpy array to CEBRA. Pytorch does currently "
+             "not support non-writable tensors. As a result, CEBRA needs to copy the "
+             "contents of the array, which might yield unnecessary memory overhead. "
+             "Ideally, adapt the code such that the array you pass to CEBRA is writable "
+             "to make your code memory efficient. "
+             "You can find more context and the rationale for this fix here: "
+             "https://github.com/AdaptiveMotorControlLab/CEBRA/pull/289."
+             "\n\n"
+             "Trace:\n" + stack_str),
+            UserWarning,
+            stacklevel=2,
+        )
+        array = array.copy()
+    return array
+
+
 class SklearnDataset(cebra.data.SingleSessionDataset):
     """Dataset for wrapping array-like input/index pairs.
 
@@ -110,6 +134,7 @@ def _parse_data(self, X: npt.NDArray):
         # one sample is a conservative default here to ensure that sklearn tests
         # passes with the correct error messages.
         X = cebra_sklearn_utils.check_input_array(X, min_samples=2)
+        X = _ensure_writable(X)
         self.neural = torch.from_numpy(X).float().to(self.device)
 
     def _parse_labels(self, labels: Optional[tuple]):
@@ -143,6 +168,8 @@ def _parse_labels(self, labels: Optional[tuple]):
                     f"or lists that can be converted to arrays, but got {type(y)}"
                 )
 
+            y = _ensure_writable(y)
+
             # Define the index as either continuous or discrete indices, depending
             # on the dtype in the index array.
             if cebra.helper._is_floating(y):

pyproject.toml

@@ -39,6 +39,10 @@ markers = [
     "cuda",
 ]
 addopts = "--ignore=cebra/integrations/threejs --ignore=cebra/integrations/streamlit.py --ignore=cebra/datasets"
+# NOTE(stes): See https://github.com/AdaptiveMotorControlLab/CEBRA/pull/289.
+filterwarnings = [
+    "error:The given NumPy array is not writable.*PyTorch does not support non-writable tensors:UserWarning",
+]
 
 
 

tests/test_sklearn.py

@@ -1544,3 +1544,20 @@ def test_last_incomplete_batch_smaller_than_offset():
     model.fit(train.neural, train.continuous)
 
     _ = model.transform(train.neural, batch_size=300)
+
+
+def test_non_writable_array():
+    X = np.random.randn(100, 10)
+    y = np.random.randn(100, 2)
+    X.setflags(write=False)
+    y.setflags(write=False)
+    with pytest.raises(ValueError, match="assignment destination is read-only"):
+        X[:] = 0
+        y[:] = 0
+
+    cebra_model = cebra.CEBRA(max_iterations=2, batch_size=32, device="cpu")
+
+    cebra_model.fit(X, y)
+    embedding = cebra_model.transform(X)
+    assert isinstance(embedding, np.ndarray)
+    assert embedding.shape[0] == X.shape[0]