Removed indices val from self-labeling step.

Author: otiliastr

neural_structured_learning/research/gam/data/dataset.py

@@ -322,6 +322,7 @@ def __init__(self, dataset, keep_label_proportions=False, inductive=False):
     if not self.inductive:
       self.dataset.indices_unlabeled = np.concatenate(
           (dataset.indices_unlabeled,
+           dataset.indices_val,
            dataset.indices_test))
 
     # If when labeling new data we want to keep the proportions of the labels

neural_structured_learning/research/gam/trainer/trainer_cotrain.py

@@ -345,7 +345,12 @@ def _select_samples_to_label(self, data, trainer_cls, session):
         assign to each of the selected nodes.
     """
     # Select the candidate samples for self-labeling, and make predictions.
+    # Remove the validation samples from the unlabeled data, if there, to avoid
+    # self-labeling them.
     indices_unlabeled = data.get_indices_unlabeled()
+    val_ind = set(data.get_indices_val())
+    indices_unlabeled = np.asarray([ind for ind in indices_unlabeled
+                                    if ind not in val_ind])
     predictions = trainer_cls.predict(
       session, indices_unlabeled, is_train=False)
     # Select most confident nodes. Compute confidence and most confident label,