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n3fit/src/n3fit/model_gen.py

@@ -26,7 +26,6 @@
     base_layer_selector,
 )
 from n3fit.backends import operations as op
-from n3fit.backends import regularizer_selector as reg_sec
 from n3fit.layers import (
     DIS,
     DY,
@@ -42,6 +41,8 @@
 from n3fit.msr import generate_msr_model_and_grid
 from validphys.photon.compute import Photon
 
+from n3fit.backends import regularizer_selector  # isort: skip isort and black don't agree
+
 
 @dataclass
 class ObservableWrapper:
@@ -127,7 +128,7 @@ def __call__(self, pdf_layer, mask=None):
 def observable_generator(
     spec_dict,
     boundary_condition=None,
-    mask_array=None,
+    training_mask_array=None,
     validation_mask_array=None,
     training_data=None,
     validation_data=None,
@@ -143,7 +144,6 @@ def observable_generator(
     the result of the observable for each contained dataset (n_points,).
 
     In summary the model has the following structure:
-        One experiment layer, made of any number of observable layers.
         Observable layers, corresponding to commondata datasets
         and made of any number of fktables (and an operation on them).
 
@@ -171,10 +171,12 @@ def observable_generator(
         boundary_condition: dict
             dictionary containing the instance of the a PDF set to be used as a
             Boundary Condition.
-        mask_array: np.ndarray
+        training_mask_array: np.ndarray
             training mask per replica
         validation_mask_array: np.ndarray
-            validation mask per replica, when not given ¬mask_array will be used
+            validation mask per replica, when not given ~training_mask_array will be used
+            while in general the validation is a negation of the training, in special cases
+            such as 1-point datasets, these are accepted by both masks and then removed by the loss
         n_replicas: int
             number of replicas fitted simultaneously
         positivity_initial: float
@@ -250,16 +252,16 @@ def observable_generator(
     model_inputs = np.concatenate(model_inputs).reshape(1, -1)
 
     # Make the mask layers...
-    if mask_array is None:
+    if training_mask_array is None:
         tr_mask_layer = None
         if validation_mask_array is None:
             vl_mask_layer = None
         else:
             vl_mask_layer = Mask(validation_mask_array, name=f"vlmask_{spec_name}")
     else:
-        tr_mask_layer = Mask(mask_array, name=f"trmask_{spec_name}")
+        tr_mask_layer = Mask(training_mask_array, name=f"trmask_{spec_name}")
         if validation_mask_array is None:
-            vl_mask_layer = Mask(~mask_array, name=f"vlmask_{spec_name}")
+            vl_mask_layer = Mask(~training_mask_array, name=f"vlmask_{spec_name}")
         else:
             vl_mask_layer = Mask(validation_mask_array, name=f"vlmask_{spec_name}")
 
@@ -735,7 +737,7 @@ def generate_nn(
     """
     nodes_list = list(nodes)  # so we can modify it
     x_input = Input(shape=(None, nodes_in), batch_size=1, name="NN_input")
-    reg = reg_sec(regularizer, **regularizer_args)
+    reg = regularizer_selector(regularizer, **regularizer_args)
 
     if layer_type == "dense_per_flavour":
         # set the arguments that will define the layer

n3fit/src/n3fit/model_trainer.py

@@ -9,18 +9,18 @@
 between iterations while at the same time keeping the amount of redundant calls to a minimum
 """
 
+import logging
 from collections import namedtuple
 from itertools import zip_longest
-import logging
 
 import numpy as np
 
+import n3fit.hyper_optimization.penalties
+import n3fit.hyper_optimization.rewards
 from n3fit import model_gen
 from n3fit.backends import NN_LAYER_ALL_REPLICAS, Lambda, MetaModel, callbacks, clear_backend_state
 from n3fit.backends import operations as op
 from n3fit.hyper_optimization.hyper_scan import HYPEROPT_STATUSES
-import n3fit.hyper_optimization.penalties
-import n3fit.hyper_optimization.rewards
 from n3fit.hyper_optimization.rewards import HyperLoss
 from n3fit.scaler import generate_scaler
 from n3fit.stopping import Stopping
@@ -563,7 +563,7 @@ def _generate_observables(
             exp_layer = model_gen.observable_generator(
                 exp_dict,
                 self.boundary_condition,
-                mask_array=experiment_data["trmask"][i],
+                training_mask_array=experiment_data["trmask"][i],
                 validation_mask_array=experiment_data["vlmask"][i],
                 training_data=experiment_data["expdata"][i],
                 validation_data=experiment_data["expdata_vl"][i],
@@ -599,7 +599,7 @@ def _generate_observables(
                 pos_dict,
                 self.boundary_condition,
                 positivity_initial=pos_initial,
-                mask_array=replica_masks,
+                training_mask_array=replica_masks,
                 training_data=training_data,
                 validation_data=training_data,
                 n_replicas=len(self.replicas),

pyproject.toml

@@ -143,7 +143,6 @@ line_length = 100
 profile = "black" # https://black.readthedocs.io/en/stable/guides/using_black_with_other_tools.html#custom-configuration
 skip_gitignore = true
 known_first_party = ["validphys", "eko", "n3fit", "nnpdf_data", "reportengine"]
-force_sort_within_sections = true
 
 [tool.pytest.ini_options]
 addopts = "--disable-warnings"