refactor(aggregation): Make utilitary functions public

src/torchjd/aggregation/_dual_cone_utils.py

@@ -6,7 +6,7 @@
 from torch import Tensor
 
 
-def _project_weights(U: Tensor, G: Tensor, solver: Literal["quadprog"]) -> Tensor:
+def project_weights(U: Tensor, G: Tensor, solver: Literal["quadprog"]) -> Tensor:
     """
     Computes the tensor of weights corresponding to the projection of the vectors in `U` onto the
     rows of a matrix whose Gramian is provided.

src/torchjd/aggregation/_pref_vector_utils.py

@@ -5,7 +5,7 @@
 from .constant import _ConstantWeighting
 
 
-def _pref_vector_to_weighting(pref_vector: Tensor | None, default: _Weighting) -> _Weighting:
+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
     weighting if the preference vector is None.
@@ -22,7 +22,7 @@ def _pref_vector_to_weighting(pref_vector: Tensor | None, default: _Weighting) -
         return _ConstantWeighting(pref_vector)
 
 
-def _pref_vector_to_str_suffix(pref_vector: Tensor | None) -> str:
+def pref_vector_to_str_suffix(pref_vector: Tensor | None) -> str:
     """Returns a suffix string containing the representation of the optional preference vector."""
 
     if pref_vector is None:

src/torchjd/aggregation/aligned_mtl.py

@@ -28,7 +28,7 @@
 import torch
 from torch import Tensor
 
-from ._pref_vector_utils import _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
 
@@ -61,7 +61,7 @@ class AlignedMTL(_WeightedAggregator):
     """
 
     def __init__(self, pref_vector: Tensor | None = None):
-        weighting = _pref_vector_to_weighting(pref_vector, default=_MeanWeighting())
+        weighting = pref_vector_to_weighting(pref_vector, default=_MeanWeighting())
         self._pref_vector = pref_vector
 
         super().__init__(weighting=_AlignedMTLWrapper(weighting))
@@ -70,7 +70,7 @@ def __repr__(self) -> str:
         return f"{self.__class__.__name__}(pref_vector={repr(self._pref_vector)})"
 
     def __str__(self) -> str:
-        return f"AlignedMTL{_pref_vector_to_str_suffix(self._pref_vector)}"
+        return f"AlignedMTL{pref_vector_to_str_suffix(self._pref_vector)}"
 
 
 class _AlignedMTLWrapper(_Weighting):

src/torchjd/aggregation/config.py

@@ -28,7 +28,7 @@
 import torch
 from torch import Tensor
 
-from ._pref_vector_utils import _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 Aggregator
 from .sum import _SumWeighting
 
@@ -62,7 +62,7 @@ class ConFIG(Aggregator):
 
     def __init__(self, pref_vector: Tensor | None = None):
         super().__init__()
-        self.weighting = _pref_vector_to_weighting(pref_vector, default=_SumWeighting())
+        self.weighting = pref_vector_to_weighting(pref_vector, default=_SumWeighting())
         self._pref_vector = pref_vector
 
     def forward(self, matrix: Tensor) -> Tensor:
@@ -80,4 +80,4 @@ def __repr__(self) -> str:
         return f"{self.__class__.__name__}(pref_vector={repr(self._pref_vector)})"
 
     def __str__(self) -> str:
-        return f"ConFIG{_pref_vector_to_str_suffix(self._pref_vector)}"
+        return f"ConFIG{pref_vector_to_str_suffix(self._pref_vector)}"

src/torchjd/aggregation/dualproj.py

@@ -2,9 +2,9 @@
 
 from torch import Tensor
 
-from ._dual_cone_utils import _project_weights
+from ._dual_cone_utils import project_weights
 from ._gramian_utils import _compute_regularized_normalized_gramian
-from ._pref_vector_utils import _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
 
@@ -47,7 +47,7 @@ def __init__(
         reg_eps: float = 0.0001,
         solver: Literal["quadprog"] = "quadprog",
     ):
-        weighting = _pref_vector_to_weighting(pref_vector, default=_MeanWeighting())
+        weighting = pref_vector_to_weighting(pref_vector, default=_MeanWeighting())
         self._pref_vector = pref_vector
 
         super().__init__(
@@ -64,7 +64,7 @@ def __repr__(self) -> str:
         )
 
     def __str__(self) -> str:
-        return f"DualProj{_pref_vector_to_str_suffix(self._pref_vector)}"
+        return f"DualProj{pref_vector_to_str_suffix(self._pref_vector)}"
 
 
 class _DualProjWrapper(_Weighting):
@@ -101,5 +101,5 @@ def __init__(
     def forward(self, matrix: Tensor) -> Tensor:
         u = self.weighting(matrix)
         G = _compute_regularized_normalized_gramian(matrix, self.norm_eps, self.reg_eps)
-        w = _project_weights(u, G, self.solver)
+        w = project_weights(u, G, self.solver)
         return w

src/torchjd/aggregation/upgrad.py

@@ -3,9 +3,9 @@
 import torch
 from torch import Tensor
 
-from ._dual_cone_utils import _project_weights
+from ._dual_cone_utils import project_weights
 from ._gramian_utils import _compute_regularized_normalized_gramian
-from ._pref_vector_utils import _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
 
@@ -47,7 +47,7 @@ def __init__(
         reg_eps: float = 0.0001,
         solver: Literal["quadprog"] = "quadprog",
     ):
-        weighting = _pref_vector_to_weighting(pref_vector, default=_MeanWeighting())
+        weighting = pref_vector_to_weighting(pref_vector, default=_MeanWeighting())
         self._pref_vector = pref_vector
 
         super().__init__(
@@ -64,7 +64,7 @@ def __repr__(self) -> str:
         )
 
     def __str__(self) -> str:
-        return f"UPGrad{_pref_vector_to_str_suffix(self._pref_vector)}"
+        return f"UPGrad{pref_vector_to_str_suffix(self._pref_vector)}"
 
 
 class _UPGradWrapper(_Weighting):
@@ -97,5 +97,5 @@ def __init__(
     def forward(self, matrix: Tensor) -> Tensor:
         U = torch.diag(self.weighting(matrix))
         G = _compute_regularized_normalized_gramian(matrix, self.norm_eps, self.reg_eps)
-        W = _project_weights(U, G, self.solver)
+        W = project_weights(U, G, self.solver)
         return torch.sum(W, dim=0)

tests/unit/aggregation/test_dual_cone_utils.py

@@ -3,7 +3,7 @@
 from pytest import mark, raises
 from torch.testing import assert_close
 
-from torchjd.aggregation._dual_cone_utils import _project_weight_vector, _project_weights
+from torchjd.aggregation._dual_cone_utils import _project_weight_vector, project_weights
 
 
 @mark.parametrize("shape", [(5, 7), (9, 37), (2, 14), (32, 114), (50, 100)])
@@ -33,7 +33,7 @@ def test_solution_weights(shape: tuple[int, int]):
     G = J @ J.T
     u = torch.rand(shape[0])
 
-    w = _project_weights(u, G, "quadprog")
+    w = project_weights(u, G, "quadprog")
     dual_gap = w - u
 
     # Dual feasibility
@@ -64,8 +64,8 @@ def test_tensorization_shape(shape: tuple[int, ...]):
 
     G = matrix @ matrix.T
 
-    W_tensor = _project_weights(U_tensor, G, "quadprog")
-    W_matrix = _project_weights(U_matrix, G, "quadprog")
+    W_tensor = project_weights(U_tensor, G, "quadprog")
+    W_matrix = project_weights(U_matrix, G, "quadprog")
 
     assert_close(W_matrix.reshape(shape), W_tensor)
 

tests/unit/aggregation/test_pref_vector_utils.py

@@ -5,7 +5,7 @@
 from torch import Tensor
 from unit._utils import ExceptionContext
 
-from torchjd.aggregation._pref_vector_utils import _pref_vector_to_weighting
+from torchjd.aggregation._pref_vector_utils import pref_vector_to_weighting
 from torchjd.aggregation.mean import _MeanWeighting
 
 
@@ -23,4 +23,4 @@
 )
 def test_pref_vector_to_weighting_check(pref_vector: Tensor | None, expectation: ExceptionContext):
     with expectation:
-        _ = _pref_vector_to_weighting(pref_vector, default=_MeanWeighting())
+        _ = pref_vector_to_weighting(pref_vector, default=_MeanWeighting())