diff options
| -rw-r--r-- | lib/parameters.py | 22 |
1 files changed, 20 insertions, 2 deletions
diff --git a/lib/parameters.py b/lib/parameters.py index 5673593..a00e376 100644 --- a/lib/parameters.py +++ b/lib/parameters.py @@ -1207,8 +1207,25 @@ class ModelAttribute: ) # required, "unique_values" in for loop is insufficient for std_by_param foo std_static = np.std(data) std_lut = np.mean([np.std(v) for v in by_param.values()]) - for param_index in range(param_count): + if loss_ignore_scalar: + ffs_eligible_params = list() + ffs_unsuitable_params = list() + for param_index in range(param_count): + if param_index in self.ignore_param: + continue + unique_values = list(set(map(lambda p: p[param_index], parameters))) + if None in unique_values: + ffs_unsuitable_params.append(param_index) + elif ( + self.param_type[param_index] == ParamType.SCALAR + and len(unique_values) >= self.min_values_for_analytic_model + ): + ffs_eligible_params.append(param_index) + else: + ffs_unsuitable_params.append(param_index) + + for param_index in range(param_count): if param_index in self.ignore_param: loss.append(np.inf) continue @@ -1272,7 +1289,8 @@ class ModelAttribute: child_data_by_scalar = partition_by_param( child_data, child_param, - ignore_parameters=self.nonscalar_param_indexes, + ignore_parameters=list(self.ignore_param.keys()) + + ffs_unsuitable_params, ) for sub_data in child_data_by_scalar.values(): children.extend((np.array(sub_data) - np.mean(sub_data)) ** 2) |