Completed integration. Tests are left to make sure everything runs pr…

…operly

examples/train_nces.py

@@ -3,11 +3,10 @@
 (2) pip install ontolearn
 """
 
-
-from ontolearn.concept_learner import NCES, NCES2, ROCES
 import argparse
 import json, os
-
+from ontolearn.concept_learner import NCES, NCES2, ROCES
+from transformers import set_seed
 
 def str2bool(v):
     if isinstance(v, bool):
@@ -54,24 +53,26 @@ def start(args):
             print("Could not find training data. Will generate some data and train.")
             training_data = NCES2.generate_training_data(knowledge_base_path, beyond_alc=True)
         if args.synthesizer == "NCES":
-            synthesizer = NCES(knowledge_base_path=knowledge_base_path, learner_names=args.models, path_of_embeddings=path_of_embeddings,
+            synthesizer = NCES(knowledge_base_path=knowledge_base_path, learner_names=args.models, path_of_embeddings=path_of_embeddings, path_of_trained_models=args.path_of_trained_models,
                 max_length=48, proj_dim=128, rnn_n_layers=2, drop_prob=0.1, num_heads=4, num_seeds=1, m=32, load_pretrained=args.load_pretrained, verbose=True)
         elif args.synthesizer == "NCES2":
             synthesizer = NCES2(knowledge_base_path=knowledge_base_path, path_of_trained_models=args.path_of_trained_models, nces2_or_roces=True, max_length=48, proj_dim=128, 
                 drop_prob=0.1, num_heads=4, num_seeds=1, m=32, verbose=True, load_pretrained=args.load_pretrained)
         else:
             synthesizer = ROCES(knowledge_base_path=knowledge_base_path, path_of_trained_models=args.path_of_trained_models, nces2_or_roces=True, k=5, max_length=48, proj_dim=128, 
                 drop_prob=0.1, num_heads=4, num_seeds=1, m=32, load_pretrained=args.load_pretrained, verbose=True)
-        synthesizer.train(training_data, epochs=args.epochs, learning_rate=args.learning_rate, num_workers=2, save_model=True)
+        synthesizer.train(training_data, epochs=args.epochs, learning_rate=args.learning_rate, num_workers=2, save_model=True, storage_path=args.storage_dir)
 
 
 if __name__ == '__main__':
+    set_seed(42)
     parser = argparse.ArgumentParser()
     parser.add_argument('--kbs', type=str, nargs='+', default=None, help='Paths of knowledge bases (OWL files)')
     parser.add_argument('--embeddings', type=str, nargs='+', default=None, help='Paths of embeddings for each KB.')
     parser.add_argument('--synthesizer', type=str, default="NCES", help='Neural synthesizer to train')
     parser.add_argument('--path_train_data', type=str, help='Path to training data')
     parser.add_argument('--path_of_trained_models', type=str, default=None, help='Path to training data')
+    parser.add_argument('--storage_dir', type=str, default=None, help='Path to training data')
     parser.add_argument('--models', type=str, nargs='+', default=['SetTransformer', 'LSTM', 'GRU'],
                         help='Neural models')
     parser.add_argument('--load_pretrained', type=str2bool, default=False, help='Whether to load the pretrained model')

ontolearn/base_nces.py

@@ -50,7 +50,7 @@ def __init__(self, knowledge_base_path, nces2_or_roces, quality_func, num_predic
             concrete_role_names = [rel.iri.get_remainder() for rel in kb.ontology.data_properties_in_signature()]
             vocab.extend(concrete_role_names)
             vocab.extend(['⁻', '≤', '≥', 'True', 'False', 'true', 'false', '{', '}', ':', '[', ']', 'double', 'integer', 'date', 'xsd'])
-        vocab = sorted(vocab) + ['PAD']
+        vocab = sorted(set(vocab)) + ['PAD']
         self.knowledge_base_path = knowledge_base_path
         self.kb = kb
         self.all_individuals = set([ind.str.split("/")[-1] for ind in kb.individuals()])
@@ -85,17 +85,22 @@ def add_data_values(self, data):
         print("\nUpdating vocabulary based on training data...\n")
         quantified_restriction_values = [str(i) for i in range(1,12)]
         vocab = list(self.vocab.keys())
-        vocab.extend(quantified_restriction_values)
+        vocab_set = set(vocab)
+        len_before_update = len(vocab_set)
+        vocab_set.update(set(quantified_restriction_values))
         values = set()
         for ce, examples in data:
             if '[' in ce:
                 for val in re.findall("\[(.*?)\]", ce):
                     values.add(val.split(' ')[-1])
-        vocab.extend(list(values))
-        vocab = sorted(vocab)
+        vocab_set.update(values)
+        vocab = sorted(vocab_set)
         self.inv_vocab = np.array(vocab, dtype='object')
         self.vocab = {vocab[i]: i for i in range(len(vocab))}
-        print("Done.\n")
+        if len_before_update < len(vocab):
+            print("Done.\n")
+        else:
+            print("No update necessary!\n")
 
 
     def collate_batch_inference(self, batch):  # pragma: no cover