removed mask from proximal operator

src/neurostatslib/solver.py

@@ -368,8 +368,6 @@ class ProxGradientSolver(Solver, abc.ABC):
     ----------
     allowed_optimizers : List[...,str]
         A list of optimizer names that are allowed to be used with this solver.
-    mask : Optional[Union[NDArray, jnp.ndarray]]
-        An optional mask array for element-wise operations. Shape (n_groups, n_features)
     """
 
     allowed_optimizers = ["ProximalGradient"]
@@ -379,65 +377,11 @@ def __init__(
         solver_name: str,
         solver_kwargs: Optional[dict] = None,
         regularizer_strength: float = 1.0,
-        mask: Optional[Union[NDArray, jnp.ndarray]] = None,
+        **kwargs
     ):
         super().__init__(solver_name, solver_kwargs=solver_kwargs)
-        self.mask = mask
         self.regularizer_strength = regularizer_strength
 
-    @property
-    def mask(self):
-        return self._mask
-
-    @mask.setter
-    def mask(self, mask: jnp.ndarray):
-        self._check_mask(mask)
-        self._mask = mask
-
-    @staticmethod
-    def _check_mask(mask: jnp.ndarray):
-        """
-        Validate the mask array.
-
-        This method ensures the mask adheres to requirements:
-        - It should be 2-dimensional.
-        - Each element must be either 0 or 1.
-        - Each feature should belong to only one group.
-        - The mask should not be empty.
-        - The mask is an array of float type.
-
-        Raises
-        ------
-        ValueError
-            If any of the above conditions are not met.
-        """
-        if mask.ndim != 2:
-            raise ValueError(
-                "`mask` must be 2-dimensional. "
-                f"{mask.ndim} dimensional mask provided instead!"
-            )
-
-        if mask.shape[0] == 0:
-            raise ValueError(f"Empty mask provided! Mask has shape {mask.shape}.")
-
-        if jnp.any((mask != 1) & (mask != 0)):
-            raise ValueError("Mask elements be 0s and 1s!")
-
-        if mask.sum() == 0:
-            raise ValueError("Empty mask provided!")
-
-        if jnp.any(mask.sum(axis=0) > 1):
-            raise ValueError(
-                "Incorrect group assignment. Some of the features are assigned "
-                "to more then one group."
-            )
-
-        if not jnp.issubdtype(mask.dtype, jnp.floating):
-            raise ValueError(
-                "Mask should be a floating point jnp.ndarray. "
-                f"Data type {mask.dtype} provided instead!"
-            )
-
     @abc.abstractmethod
     def get_prox_operator(
         self,
@@ -499,12 +443,10 @@ def __init__(
         solver_name: str = "ProximalGradient",
         solver_kwargs: Optional[dict] = None,
         regularizer_strength: float = 1.0,
-        mask: Optional[Union[NDArray, jnp.ndarray]] = None,
     ):
         super().__init__(
             solver_name,
-            solver_kwargs=solver_kwargs,
-            mask=mask,
+            solver_kwargs=solver_kwargs
         )
         self.regularizer_strength = regularizer_strength
 
@@ -564,11 +506,62 @@ def __init__(
         super().__init__(
             solver_name,
             solver_kwargs=solver_kwargs,
-            mask=mask,
         )
         self.regularizer_strength = regularizer_strength
-        mask = jnp.asarray(mask)
+        self.mask = jnp.asarray(mask)
+
+    @property
+    def mask(self):
+        return self._mask
+
+    @mask.setter
+    def mask(self, mask: jnp.ndarray):
         self._check_mask(mask)
+        self._mask = mask
+
+    @staticmethod
+    def _check_mask(mask: jnp.ndarray):
+        """
+        Validate the mask array.
+
+        This method ensures the mask adheres to requirements:
+        - It should be 2-dimensional.
+        - Each element must be either 0 or 1.
+        - Each feature should belong to only one group.
+        - The mask should not be empty.
+        - The mask is an array of float type.
+
+        Raises
+        ------
+        ValueError
+            If any of the above conditions are not met.
+        """
+        if mask.ndim != 2:
+            raise ValueError(
+                "`mask` must be 2-dimensional. "
+                f"{mask.ndim} dimensional mask provided instead!"
+            )
+
+        if mask.shape[0] == 0:
+            raise ValueError(f"Empty mask provided! Mask has shape {mask.shape}.")
+
+        if jnp.any((mask != 1) & (mask != 0)):
+            raise ValueError("Mask elements be 0s and 1s!")
+
+        if mask.sum() == 0:
+            raise ValueError("Empty mask provided!")
+
+        if jnp.any(mask.sum(axis=0) > 1):
+            raise ValueError(
+                "Incorrect group assignment. Some of the features are assigned "
+                "to more then one group."
+            )
+
+        if not jnp.issubdtype(mask.dtype, jnp.floating):
+            raise ValueError(
+                "Mask should be a floating point jnp.ndarray. "
+                f"Data type {mask.dtype} provided instead!"
+            )
 
     def get_prox_operator(
         self,