Move factory code up

baybe/searchspace/continuous.py

@@ -304,6 +304,61 @@ def _drop_parameters(self, parameter_names: Collection[str]) -> SubspaceContinuo
             ],
         )
 
+    def _ensure_nonzero_parameters(
+        self,
+        inactive_parameters: Collection[str],
+        zero_threshold: float = ZERO_THRESHOLD,
+    ) -> SubspaceContinuous:
+        """Create a new subspace with following several actions.
+
+        * Ensure active parameter != 0.0.
+        * Remove cardinality constraint.
+
+        Args:
+            inactive_parameters: A list of inactive parameters.
+            zero_threshold: Threshold for checking whether a value is zero.
+
+        Returns:
+            A new subspace object.
+        """
+        # Active parameters: parameters involved in cardinality constraints
+        active_params_sample = set(
+            self.param_names_in_cardinality_constraint
+        ).difference(set(inactive_parameters))
+
+        constraints_lin_ineq = list(self.constraints_lin_ineq)
+        for active_param in active_params_sample:
+            index = self.param_names.index(active_param)
+
+            # TODO: Ensure x != 0 when x in [..., 0, ...] is not done. Do we need it?
+            # TODO: To ensure the minimum cardinality constraints, shall we keep the x
+            #  != 0 operations or shall we instead skip the invalid results at the end
+            # Ensure x != 0 when bounds = [..., 0]. This is needed, otherwise
+            # the minimum cardinality constraint is easily violated
+            if self.parameters[index].bounds.upper == 0:
+                constraints_lin_ineq.append(
+                    ContinuousLinearInequalityConstraint(
+                        parameters=[active_param],
+                        coefficients=[-1.0],
+                        rhs=min(zero_threshold, -self.parameters[index].bounds.lower),
+                    )
+                )
+            # Ensure x != 0 when bounds = [0, ...]
+            elif self.parameters[index].bounds.lower == 0:
+                constraints_lin_ineq.append(
+                    ContinuousLinearInequalityConstraint(
+                        parameters=[active_param],
+                        coefficients=[1.0],
+                        rhs=min(zero_threshold, self.parameters[index].bounds.upper),
+                    ),
+                )
+
+        return SubspaceContinuous(
+            parameters=tuple(self.parameters),
+            constraints_lin_eq=self.constraints_lin_eq,
+            constraints_lin_ineq=tuple(constraints_lin_ineq),
+        )
+
     def transform(
         self,
         df: pd.DataFrame | None = None,
@@ -485,61 +540,6 @@ def _sample_inactive_parameters(self, batch_size: int = 1) -> list[set[str]]:
         ]
         return [set(chain(*x)) for x in zip(*inactives_per_constraint)]
 
-    def _ensure_nonzero_parameters(
-        self,
-        inactive_parameters: Collection[str],
-        zero_threshold: float = ZERO_THRESHOLD,
-    ) -> SubspaceContinuous:
-        """Create a new subspace with following several actions.
-
-        * Ensure active parameter != 0.0.
-        * Remove cardinality constraint.
-
-        Args:
-            inactive_parameters: A list of inactive parameters.
-            zero_threshold: Threshold for checking whether a value is zero.
-
-        Returns:
-            A new subspace object.
-        """
-        # Active parameters: parameters involved in cardinality constraints
-        active_params_sample = set(
-            self.param_names_in_cardinality_constraint
-        ).difference(set(inactive_parameters))
-
-        constraints_lin_ineq = list(self.constraints_lin_ineq)
-        for active_param in active_params_sample:
-            index = self.param_names.index(active_param)
-
-            # TODO: Ensure x != 0 when x in [..., 0, ...] is not done. Do we need it?
-            # TODO: To ensure the minimum cardinality constraints, shall we keep the x
-            #  != 0 operations or shall we instead skip the invalid results at the end
-            # Ensure x != 0 when bounds = [..., 0]. This is needed, otherwise
-            # the minimum cardinality constraint is easily violated
-            if self.parameters[index].bounds.upper == 0:
-                constraints_lin_ineq.append(
-                    ContinuousLinearInequalityConstraint(
-                        parameters=[active_param],
-                        coefficients=[-1.0],
-                        rhs=min(zero_threshold, -self.parameters[index].bounds.lower),
-                    )
-                )
-            # Ensure x != 0 when bounds = [0, ...]
-            elif self.parameters[index].bounds.lower == 0:
-                constraints_lin_ineq.append(
-                    ContinuousLinearInequalityConstraint(
-                        parameters=[active_param],
-                        coefficients=[1.0],
-                        rhs=min(zero_threshold, self.parameters[index].bounds.upper),
-                    ),
-                )
-
-        return SubspaceContinuous(
-            parameters=tuple(self.parameters),
-            constraints_lin_eq=self.constraints_lin_eq,
-            constraints_lin_ineq=tuple(constraints_lin_ineq),
-        )
-
     def samples_full_factorial(self, n_points: int = 1) -> pd.DataFrame:
         """Deprecated!"""  # noqa: D401
         warnings.warn(