Checks for common errors / defaults

n3fit/runcards/Basic_runcard.yml

@@ -38,7 +38,6 @@ fitting:
   trvlseed: 1
   nnseed: 2
   mcseed: 3
-  epochs: 900
   save: 'weights.h5'
 #   load: '/path/to/weights.h5/file'
 

n3fit/src/n3fit/checks.py

@@ -54,15 +54,22 @@ def check_consistent_layers(parameters):
         raise CheckError(f"Number of layers ({npl}) does not match activation functions: {apl}")
 
 
-def check_stopping(parameters):
+def check_stopping(parameters, epochs_legacy=None):
     """Checks whether the stopping-related options are sane:
     stopping patience as a ratio between 0 and 1
     and positive number of epochs
     """
     spt = parameters.get("stopping_patience")
     if spt is not None and not 0.0 <= spt <= 1.0:
         raise CheckError(f"The stopping_patience must be between 0 and 1, got: {spt}")
-    epochs = parameters["epochs"]
+    epochs = parameters.get("epochs")
+    if epochs is None:
+        raise CheckError("A number of epochs must be given under fitting::parameters")
+    if epochs_legacy is not None and not epochs == epochs_legacy:
+        raise CheckError(f"Received contradictory values for epochs: {epochs_legacy} and {epochs}. "
+                "Note that 'epochs' is only supported under fitting::parameters")
+    if not isinstance(epochs, int):
+        raise CheckError(f"Only integer number of epochs allowed, received: {epochs}")
     if epochs < 1:
         raise CheckError(f"Needs to run at least 1 epoch, got: {epochs}")
 
@@ -155,14 +162,14 @@ def check_model_file(save, load):
             raise CheckError(f"Model file {load} seems to be empty")
 
 @make_argcheck
-def wrapper_check_NN(basis, tensorboard, save, load, parameters):
-    """ Wrapper function for all NN-related checks """
+def wrapper_check_NN(basis, tensorboard, save, load, parameters, fitting):
+    """ Wrapper function for all sensible NN-related checks """
     check_tensorboard(tensorboard)
     check_model_file(save, load)
     check_existing_parameters(parameters)
     check_consistent_layers(parameters)
     check_basis_with_layers(basis, parameters)
-    check_stopping(parameters)
+    check_stopping(parameters, epochs_legacy=fitting.get("epochs"))
     check_dropout(parameters)
     check_lagrange_multipliers(parameters, "integrability")
     check_lagrange_multipliers(parameters, "positivity")
@@ -317,12 +324,16 @@ def check_sumrules(sum_rules):
 
 # Checks on the physics
 @make_argcheck
-def check_consistent_basis(sum_rules, fitbasis, basis, theoryid):
+def check_consistent_basis(sum_rules, fitbasis, basis, theoryid, parameters):
     """Checks the fitbasis setup for inconsistencies
     - Checks the sum rules can be imposed
     - Correct flavours for the selected basis
     - Correct ranges (min < max) for the small and large-x exponents
     """
+    # Check that the sum rules are not included in the wrong dictionary
+    if "sum_rules" in parameters:
+        raise CheckError("'sum_rules' included under fitting::parameters, they should be under "
+                "fiting: instead")
     check_sumrules(sum_rules)
     # Check that there are no duplicate flavours and that parameters are sane
     flavs = []