register the unpolarized bc also with the vp interface

n3fit/src/n3fit/hyper_optimization/rewards.py

@@ -196,7 +196,7 @@ def compute_loss(
         self,
         penalties: dict[str, np.ndarray],
         experimental_loss: np.ndarray,
-        pdf_model: MetaModel,
+        pdf_object: N3PDF,
         experimental_data: list[DataGroupSpec],
         fold_idx: int = 0,
     ) -> float:
@@ -214,8 +214,8 @@ def compute_loss(
                 as defined in 'penalties.py' and instantiated within :class:`~n3fit.model_trainer.ModelTrainer`.
             experimental_loss: NDArray(replicas)
                 Experimental loss for each replica.
-            pdf_model: :class:`n3fit.backends.MetaModel`
-                N3fitted meta-model.
+            pdf_object: :class:`n3fit.vpinterface.N3PDF`
+                N3fitted PDF
             experimental_data: List[validphys.core.DataGroupSpec]
                 List of tuples containing `validphys.core.DataGroupSpec` instances for each group data set
             fold_idx: int
@@ -233,18 +233,20 @@ def compute_loss(
         >>> import numpy as np
         >>> from n3fit.hyper_optimization.rewards import HyperLoss
         >>> from n3fit.model_gen import generate_pdf_model
+        >>> from n3fit.vpinterface import N3PDF
         >>> from validphys.loader import Loader
         >>> hyper = HyperLoss(loss_type="chi2", replica_statistic="average", fold_statistic="average")
         >>> penalties = {'saturation': np.array([1.0, 2.0]), 'patience': np.array([3.0, 4.0]), 'integrability': np.array([5.0, 6.0]),}
         >>> experimental_loss = np.array([0.1, 0.2])
-        >>> ds = Loader().check_dataset("NMC_NC_NOTFIXED_P_EM-SIGMARED", theoryid=399, cuts="internal")
+        >>> ds = Loader().check_dataset("NMC_NC_NOTFIXED_P_EM-SIGMARED", variant="legacy", theoryid=399, cuts="internal")
         >>> experimental_data = [Loader().check_experiment("My DataGroupSpec", [ds])]
         >>> fake_fl = [{'fl' : i, 'largex' : [0,1], 'smallx': [1,2]} for i in ['u', 'ubar', 'd', 'dbar', 'c', 'g', 's', 'sbar']]
         >>> pdf_model = generate_pdf_model(nodes=[8], activations=['linear'], seed=0, num_replicas=2, flav_info=fake_fl, fitbasis="FLAVOUR")
-        >>> loss = hyper.compute_loss(penalties, experimental_loss, pdf_model, experimental_data)
+        >>> pdf = N3PDF(pdf_model.split_replicas())
+        >>> loss = hyper.compute_loss(penalties, experimental_loss, pdf, experimental_data)
         """
         # calculate phi for a given k-fold using vpinterface and validphys
-        phi_per_fold = compute_phi(N3PDF(pdf_model.split_replicas()), experimental_data)
+        phi_per_fold = compute_phi(pdf_object, experimental_data)
 
         # update hyperopt metrics
         # these are saved in the phi_vector and chi2_matrix attributes, excluding penalties

n3fit/src/n3fit/model_trainer.py

@@ -1007,11 +1007,15 @@ def hyperparametrizable(self, params):
                 # containing only exp datasets within the held out fold
                 experimental_data = self._filter_datagroupspec(partition["datasets"])
 
+                vplike_pdf = N3PDF(pdf_model.split_replicas())
+                if self.boundary_condition is not None:
+                    vplike_pdf.register_boundary(self.boundary_condition["unpolarized_bc"])
+
                 # Compute per replica hyper losses
                 hyper_loss = self._hyper_loss.compute_loss(
                     penalties=penalties,
                     experimental_loss=experimental_loss,
-                    pdf_model=pdf_model,
+                    pdf_object=vplike_pdf,
                     experimental_data=experimental_data,
                     fold_idx=k,
                 )
@@ -1025,7 +1029,7 @@ def hyperparametrizable(self, params):
                 ]
                 trvl_data = self._filter_datagroupspec(trvl_exp_names)
                 # evaluate phi on training/validation exp set
-                trvl_phi = compute_phi(N3PDF(pdf_model.split_replicas()), trvl_data)
+                trvl_phi = compute_phi(vplike_pdf, trvl_data)
 
                 # Now save all information from this fold
                 l_hyper.append(hyper_loss)