suggestions from review

autoPyTorch/constants.py

@@ -78,3 +78,6 @@
 
 # To avoid that we get a sequence that is too long to be fed to a network
 MAX_WINDOW_SIZE_BASE = 500
+
+# AutoPyTorch optionally allows network inference or metrics calculation for the following datasets
+OPTIONAL_INFERENCE_CHOICES = ('test',)

autoPyTorch/utils/results_manager.py

@@ -12,6 +12,7 @@
 from smac.tae import StatusType
 from smac.utils.io.traj_logging import TrajEntry
 
+from autoPyTorch.constants import OPTIONAL_INFERENCE_CHOICES
 from autoPyTorch.pipeline.components.training.metrics.base import autoPyTorchMetric
 
 
@@ -28,9 +29,6 @@
 ]
 
 
-OPTIONAL_INFERENCE_CHOICES = ('test',)
-
-
 def cost2metric(cost: float, metric: autoPyTorchMetric) -> float:
     """
     Revert cost metric evaluated in SMAC to the original metric.
@@ -434,7 +432,7 @@ def _check_null_in_optional_inference_choices(
         self
     ) -> None:
         """
-        Checks if the data is missing for each optional inference choice and
+        Checks if the data is missing or if all the runs failed for each optional inference choice and
         sets the scores for that inference choice to all None.
         """
         for inference_choice in OPTIONAL_INFERENCE_CHOICES:

autoPyTorch/utils/results_visualizer.py

@@ -6,7 +6,8 @@
 
 import numpy as np
 
-from autoPyTorch.utils.results_manager import MetricResults, OPTIONAL_INFERENCE_CHOICES
+from autoPyTorch.constants import OPTIONAL_INFERENCE_CHOICES
+from autoPyTorch.utils.results_manager import MetricResults
 
 
 plt.rcParams["font.family"] = "Times New Roman"