Restructure deprecation functions

Author: CeliaBenquet

tests/_reference_implementations/__init__.py

@@ -0,0 +1,32 @@
+"""Reference implementations for testing consistency and backward compatibility.
+
+This package contains reference implementations of previously deprecated or
+parametrized model components, used for testing consistency and backward compatibility
+in the test suite.
+"""
+
+from .deprecated_transforms import (
+    cebra_transform_deprecated,
+    multiobjective_transform_deprecated,
+)
+from .reference_offset_models import (
+    Offset5ModelReference,
+    Offset10ModelReference,
+    Offset15ModelReference,
+    Offset20ModelReference,
+    Offset36Reference,
+    Offset40Reference,
+    Offset50Reference,
+)
+
+__all__ = [
+    "cebra_transform_deprecated",
+    "multiobjective_transform_deprecated",
+    "Offset5ModelReference",
+    "Offset10ModelReference",
+    "Offset15ModelReference",
+    "Offset20ModelReference",
+    "Offset36Reference",
+    "Offset40Reference",
+    "Offset50Reference",
+]

tests/_utils_deprecated.py …implementations/deprecated_transforms.pytests/_utils_deprecated.py renamed to tests/_reference_implementations/deprecated_transforms.py tests/_utils_deprecated.py renamed to tests/_reference_implementations/deprecated_transforms.py

@@ -11,8 +11,8 @@
 import cebra.models
 
 
-#NOTE: Deprecated: transform is now handled in the solver but the original
-#      method is kept here for testing.
+#NOTE(celia): Deprecated: transform is now handled in the solver but the original
+#      method is kept here for testing consistency.
 def cebra_transform_deprecated(cebra_model,
                                X: Union[npt.NDArray, torch.Tensor],
                                session_id: Optional[int] = None) -> npt.NDArray:
@@ -72,9 +72,9 @@ def cebra_transform_deprecated(cebra_model,
     return output
 
 
-# NOTE: Deprecated: batched transform can now be performed (more memory efficient)
+# NOTE(celia): Deprecated: batched transform can now be performed (more memory efficient)
 #       using the transform method of the model, and handling padding is implemented
-#       directly in the base Solver. This method is kept for testing purposes.
+#       directly in the base Solver. This method is kept for testing consistency.
 @torch.no_grad()
 def multiobjective_transform_deprecated(solver: "cebra.solvers.Solver",
                                         inputs: torch.Tensor) -> torch.Tensor:
@@ -90,7 +90,7 @@ def multiobjective_transform_deprecated(solver: "cebra.solvers.Solver",
 
     warnings.warn(
         "The method is deprecated "
-        "but kept for testing puroposes."
+        "but kept for testing purposes."
         "We recommend using `transform` instead.",
         DeprecationWarning,
         stacklevel=2)

tests/test_integration_xcebra.py

@@ -1,6 +1,6 @@
 import pickle
 
-import _utils_deprecated
+import _reference_implementations
 import numpy as np
 import pytest
 import torch
@@ -174,7 +174,7 @@ def test_synthetic_data_training(synthetic_data, device):
                        atol=1e-4)
 
     # Test and compare the previous transform (transform_deprecated)
-    deprecated_transform_embedding = _utils_deprecated.multiobjective_transform_deprecated(
+    deprecated_transform_embedding = _reference_implementations.multiobjective_transform_deprecated(
         solver, data.neural.to(device))
     assert np.allclose(embedding,
                        deprecated_transform_embedding,

tests/test_sklearn.py

@@ -25,8 +25,8 @@
 import tempfile
 import warnings
 
+import _reference_implementations
 import _util
-import _utils_deprecated
 import numpy as np
 import packaging.version
 import pytest
@@ -1412,7 +1412,8 @@ def test_new_transform(model_architecture, device):
     # time contrastive
     cebra_model.fit(X)
     embedding1 = cebra_model.transform(X)
-    embedding2 = _utils_deprecated.cebra_transform_deprecated(cebra_model, X)
+    embedding2 = _reference_implementations.cebra_transform_deprecated(
+        cebra_model, X)
     assert np.allclose(embedding1, embedding2, rtol=1e-5,
                        atol=1e-8), "Arrays are not close enough"
 
@@ -1421,20 +1422,20 @@ def test_new_transform(model_architecture, device):
     assert cebra_model.num_sessions is None
 
     embedding1 = cebra_model.transform(X)
-    embedding2 = _utils_deprecated.cebra_transform_deprecated(cebra_model, X)
+    embedding2 = _reference_implementations.cebra_transform_deprecated(
+        cebra_model, X)
     assert np.allclose(embedding1, embedding2, rtol=1e-5,
                        atol=1e-8), "Arrays are not close enough"
 
     embedding1 = cebra_model.transform(torch.Tensor(X))
-    embedding2 = _utils_deprecated.cebra_transform_deprecated(
+    embedding2 = _reference_implementations.cebra_transform_deprecated(
         cebra_model, torch.Tensor(X))
     assert np.allclose(embedding1, embedding2, rtol=1e-5,
                        atol=1e-8), "Arrays are not close enough"
 
     embedding1 = cebra_model.transform(torch.Tensor(X), session_id=0)
-    embedding2 = _utils_deprecated.cebra_transform_deprecated(cebra_model,
-                                                              torch.Tensor(X),
-                                                              session_id=0)
+    embedding2 = _reference_implementations.cebra_transform_deprecated(
+        cebra_model, torch.Tensor(X), session_id=0)
     assert np.allclose(embedding1, embedding2, rtol=1e-5,
                        atol=1e-8), "Arrays are not close enough"
 
@@ -1444,55 +1445,52 @@ def test_new_transform(model_architecture, device):
     # discrete behavior contrastive
     cebra_model.fit(X, y_d)
     embedding1 = cebra_model.transform(X)
-    embedding2 = _utils_deprecated.cebra_transform_deprecated(cebra_model, X)
+    embedding2 = _reference_implementations.cebra_transform_deprecated(
+        cebra_model, X)
     assert np.allclose(embedding1, embedding2, rtol=1e-5,
                        atol=1e-8), "Arrays are not close enough"
 
     # mixed
     cebra_model.fit(X, y_c1, y_c2, y_d)
     embedding1 = cebra_model.transform(X)
-    embedding2 = _utils_deprecated.cebra_transform_deprecated(cebra_model, X)
+    embedding2 = _reference_implementations.cebra_transform_deprecated(
+        cebra_model, X)
     assert np.allclose(embedding1, embedding2, rtol=1e-5,
                        atol=1e-8), "Arrays are not close enough"
 
     # multi-session discrete behavior contrastive
     cebra_model.fit([X, X_s2], [y_d, y_d_s2])
 
     embedding1 = cebra_model.transform(X, session_id=0)
-    embedding2 = _utils_deprecated.cebra_transform_deprecated(cebra_model,
-                                                              X,
-                                                              session_id=0)
+    embedding2 = _reference_implementations.cebra_transform_deprecated(
+        cebra_model, X, session_id=0)
     assert np.allclose(embedding1, embedding2, rtol=1e-5,
                        atol=1e-8), "Arrays are not close enough"
 
     embedding1 = cebra_model.transform(torch.Tensor(X), session_id=0)
-    embedding2 = _utils_deprecated.cebra_transform_deprecated(cebra_model,
-                                                              torch.Tensor(X),
-                                                              session_id=0)
+    embedding2 = _reference_implementations.cebra_transform_deprecated(
+        cebra_model, torch.Tensor(X), session_id=0)
     assert np.allclose(embedding1, embedding2, rtol=1e-5,
                        atol=1e-8), "Arrays are not close enough"
 
     embedding1 = cebra_model.transform(X_s2, session_id=1)
-    embedding2 = _utils_deprecated.cebra_transform_deprecated(cebra_model,
-                                                              X_s2,
-                                                              session_id=1)
+    embedding2 = _reference_implementations.cebra_transform_deprecated(
+        cebra_model, X_s2, session_id=1)
     assert np.allclose(embedding1, embedding2, rtol=1e-5,
                        atol=1e-8), "Arrays are not close enough"
 
     # multi-session continuous behavior contrastive
     cebra_model.fit([X, X_s2], [y_c1, y_c1_s2])
 
     embedding1 = cebra_model.transform(X, session_id=0)
-    embedding2 = _utils_deprecated.cebra_transform_deprecated(cebra_model,
-                                                              X,
-                                                              session_id=0)
+    embedding2 = _reference_implementations.cebra_transform_deprecated(
+        cebra_model, X, session_id=0)
     assert np.allclose(embedding1, embedding2, rtol=1e-5,
                        atol=1e-8), " are not close enough"
 
     embedding1 = cebra_model.transform(torch.Tensor(X), session_id=0)
-    embedding2 = _utils_deprecated.cebra_transform_deprecated(cebra_model,
-                                                              torch.Tensor(X),
-                                                              session_id=0)
+    embedding2 = _reference_implementations.cebra_transform_deprecated(
+        cebra_model, torch.Tensor(X), session_id=0)
     assert np.allclose(embedding1, embedding2, rtol=1e-5,
                        atol=1e-8), "Arrays are not close enough"
 
@@ -1511,47 +1509,41 @@ def test_new_transform(model_architecture, device):
     cebra_model.fit([X, X_s2, X], [y_d, y_d_s2, y_d])
 
     embedding1 = cebra_model.transform(X, session_id=0)
-    embedding2 = _utils_deprecated.cebra_transform_deprecated(cebra_model,
-                                                              X,
-                                                              session_id=0)
+    embedding2 = _reference_implementations.cebra_transform_deprecated(
+        cebra_model, X, session_id=0)
     assert np.allclose(embedding1, embedding2, rtol=1e-5,
                        atol=1e-8), "Arrays are not close enough"
 
     embedding1 = cebra_model.transform(X_s2, session_id=1)
-    embedding2 = _utils_deprecated.cebra_transform_deprecated(cebra_model,
-                                                              X_s2,
-                                                              session_id=1)
+    embedding2 = _reference_implementations.cebra_transform_deprecated(
+        cebra_model, X_s2, session_id=1)
     assert np.allclose(embedding1, embedding2, rtol=1e-5,
                        atol=1e-8), "Arrays are not close enough"
 
     embedding1 = cebra_model.transform(X, session_id=2)
-    embedding2 = _utils_deprecated.cebra_transform_deprecated(cebra_model,
-                                                              X,
-                                                              session_id=2)
+    embedding2 = _reference_implementations.cebra_transform_deprecated(
+        cebra_model, X, session_id=2)
     assert np.allclose(embedding1, embedding2, rtol=1e-5,
                        atol=1e-8), "Arrays are not close enough"
 
     # multi-session continuous behavior contrastive, more than two sessions
     cebra_model.fit([X, X_s2, X], [y_c1, y_c1_s2, y_c1])
 
     embedding1 = cebra_model.transform(X, session_id=0)
-    embedding2 = _utils_deprecated.cebra_transform_deprecated(cebra_model,
-                                                              X,
-                                                              session_id=0)
+    embedding2 = _reference_implementations.cebra_transform_deprecated(
+        cebra_model, X, session_id=0)
     assert np.allclose(embedding1, embedding2, rtol=1e-5,
                        atol=1e-8), "Arrays are not close enough"
 
     embedding1 = cebra_model.transform(X_s2, session_id=1)
-    embedding2 = _utils_deprecated.cebra_transform_deprecated(cebra_model,
-                                                              X_s2,
-                                                              session_id=1)
+    embedding2 = _reference_implementations.cebra_transform_deprecated(
+        cebra_model, X_s2, session_id=1)
     assert np.allclose(embedding1, embedding2, rtol=1e-5,
                        atol=1e-8), "Arrays are not close enough"
 
     embedding1 = cebra_model.transform(X, session_id=2)
-    embedding2 = _utils_deprecated.cebra_transform_deprecated(cebra_model,
-                                                              X,
-                                                              session_id=2)
+    embedding2 = _reference_implementations.cebra_transform_deprecated(
+        cebra_model, X, session_id=2)
     assert np.allclose(embedding1, embedding2, rtol=1e-5,
                        atol=1e-8), "Arrays are not close enough"