Make _pref_vector_to_weighting check input itself

src/torchjd/aggregation/_pref_vector_utils.py

@@ -5,17 +5,6 @@
 from .constant import _ConstantWeighting
 
 
-def _check_pref_vector(pref_vector: Tensor | None) -> None:
-    """Checks the correctness of the parameter pref_vector."""
-
-    if pref_vector is not None:
-        if pref_vector.ndim != 1:
-            raise ValueError(
-                "Parameter `pref_vector` must be a vector (1D Tensor). Found `pref_vector.ndim = "
-                f"{pref_vector.ndim}`."
-            )
-
-
 def _pref_vector_to_weighting(pref_vector: Tensor | None, default: _Weighting) -> _Weighting:
     """
     Returns the weighting associated to a given preference vector, with a fallback to a default
@@ -25,6 +14,11 @@ def _pref_vector_to_weighting(pref_vector: Tensor | None, default: _Weighting) -
     if pref_vector is None:
         return default
     else:
+        if pref_vector.ndim != 1:
+            raise ValueError(
+                "Parameter `pref_vector` must be a vector (1D Tensor). Found `pref_vector.ndim = "
+                f"{pref_vector.ndim}`."
+            )
         return _ConstantWeighting(pref_vector)
 
 

src/torchjd/aggregation/aligned_mtl.py

@@ -29,11 +29,7 @@
 from torch import Tensor
 from torch.linalg import LinAlgError
 
-from ._pref_vector_utils import (
-    _check_pref_vector,
-    _pref_vector_to_str_suffix,
-    _pref_vector_to_weighting,
-)
+from ._pref_vector_utils import _pref_vector_to_str_suffix, _pref_vector_to_weighting
 from .bases import _WeightedAggregator, _Weighting
 from .mean import _MeanWeighting
 
@@ -66,7 +62,6 @@ class AlignedMTL(_WeightedAggregator):
     """
 
     def __init__(self, pref_vector: Tensor | None = None):
-        _check_pref_vector(pref_vector)
         weighting = _pref_vector_to_weighting(pref_vector, default=_MeanWeighting())
         self._pref_vector = pref_vector
 

src/torchjd/aggregation/config.py

@@ -28,13 +28,9 @@
 import torch
 from torch import Tensor
 
-from torchjd.aggregation._pref_vector_utils import (
-    _check_pref_vector,
-    _pref_vector_to_str_suffix,
-    _pref_vector_to_weighting,
-)
-from torchjd.aggregation.bases import Aggregator
-from torchjd.aggregation.sum import _SumWeighting
+from ._pref_vector_utils import _pref_vector_to_str_suffix, _pref_vector_to_weighting
+from .bases import Aggregator
+from .sum import _SumWeighting
 
 
 class ConFIG(Aggregator):
@@ -66,7 +62,6 @@ class ConFIG(Aggregator):
 
     def __init__(self, pref_vector: Tensor | None = None):
         super().__init__()
-        _check_pref_vector(pref_vector)
         self.weighting = _pref_vector_to_weighting(pref_vector, default=_SumWeighting())
         self._pref_vector = pref_vector
 

src/torchjd/aggregation/dualproj.py

@@ -4,11 +4,7 @@
 
 from ._dual_cone_utils import _project_weights
 from ._gramian_utils import _compute_regularized_normalized_gramian
-from ._pref_vector_utils import (
-    _check_pref_vector,
-    _pref_vector_to_str_suffix,
-    _pref_vector_to_weighting,
-)
+from ._pref_vector_utils import _pref_vector_to_str_suffix, _pref_vector_to_weighting
 from .bases import _WeightedAggregator, _Weighting
 from .mean import _MeanWeighting
 
@@ -51,7 +47,6 @@ def __init__(
         reg_eps: float = 0.0001,
         solver: Literal["quadprog"] = "quadprog",
     ):
-        _check_pref_vector(pref_vector)
         weighting = _pref_vector_to_weighting(pref_vector, default=_MeanWeighting())
         self._pref_vector = pref_vector
 

src/torchjd/aggregation/upgrad.py

@@ -5,11 +5,7 @@
 
 from ._dual_cone_utils import _project_weights
 from ._gramian_utils import _compute_regularized_normalized_gramian
-from ._pref_vector_utils import (
-    _check_pref_vector,
-    _pref_vector_to_str_suffix,
-    _pref_vector_to_weighting,
-)
+from ._pref_vector_utils import _pref_vector_to_str_suffix, _pref_vector_to_weighting
 from .bases import _WeightedAggregator, _Weighting
 from .mean import _MeanWeighting
 
@@ -51,7 +47,6 @@ def __init__(
         reg_eps: float = 0.0001,
         solver: Literal["quadprog"] = "quadprog",
     ):
-        _check_pref_vector(pref_vector)
         weighting = _pref_vector_to_weighting(pref_vector, default=_MeanWeighting())
         self._pref_vector = pref_vector
 

tests/unit/aggregation/test_pref_vector_utils.py

@@ -0,0 +1,26 @@
+from contextlib import nullcontext as does_not_raise
+
+import torch
+from pytest import mark, raises
+from torch import Tensor
+from unit._utils import ExceptionContext
+
+from torchjd.aggregation._pref_vector_utils import _pref_vector_to_weighting
+from torchjd.aggregation.mean import _MeanWeighting
+
+
+@mark.parametrize(
+    ["pref_vector", "expectation"],
+    [
+        (None, does_not_raise()),
+        (torch.ones([]), raises(ValueError)),
+        (torch.ones([0]), does_not_raise()),
+        (torch.ones([1]), does_not_raise()),
+        (torch.ones([5]), does_not_raise()),
+        (torch.ones([1, 1]), raises(ValueError)),
+        (torch.ones([1, 1, 1]), raises(ValueError)),
+    ],
+)
+def test_pref_vector_to_weighting_check(pref_vector: Tensor | None, expectation: ExceptionContext):
+    with expectation:
+        _ = _pref_vector_to_weighting(pref_vector, default=_MeanWeighting())