Concept learner's extensions

examples/train_clip.py

@@ -0,0 +1,93 @@
+import argparse
+import json, os
+from ontolearn.concept_learner import NCES, NCES2, ROCES, CLIP
+from transformers import set_seed
+import time
+from ontolearn.knowledge_base import KnowledgeBase
+from ontolearn.lp_generator import LPGen
+from ontolearn.refinement_operators import ExpressRefinement
+
+
+def str2bool(v):
+    if isinstance(v, bool):
+        return v
+    elif v.lower() in ['t', 'true', 'y', 'yes', '1']:
+        return True
+    elif v.lower() in ['f', 'false', 'n', 'no', '0']:
+        return False
+    else:
+        raise ValueError('Invalid boolean value.')
+
+
+def start(args):
+    assert (args.kb is not None), "Argument 'kb' is required."
+    training_data = None
+    if args.path_train_data is not None:
+        try:
+            if os.path.isdir(args.path_train_data):
+                with open(args.path_train_data + "/LPs.json") as file:
+                    training_data = json.load(file)
+                    if isinstance(training_data, dict):
+                        training_data = list(training_data.items())
+                    else:
+                        assert isinstance(training_data,
+                                          list), "The training data must either be stored as a dictionary ({'expr': {'positive examples': [], 'negative examples': []}, ...,}) or a list of items"
+            else:
+                with open(args.path_train_data) as file:
+                    training_data = json.load(file)
+                if isinstance(training_data, dict):
+                    training_data = list(training_data.items())
+                else:
+                    assert isinstance(training_data,
+                                      list), "The training data must either be stored as a dictionary ({'expr': {'positive examples': [], 'negative examples': []}, ...,}) or a list of items"
+        except FileNotFoundError:
+            print(
+                "Couldn't find training data in the specified path. Defaulting to generating training data.")
+
+    # use clip here
+    knowledge_base_path = args.kb
+    path_of_embeddings = args.path_of_embeddings
+    if os.path.exists(knowledge_base_path) and os.path.exists(path_of_embeddings):
+        KB = KnowledgeBase(path=knowledge_base_path)
+        op = ExpressRefinement(knowledge_base=KB, use_inverse=False,
+                               use_numeric_datatypes=False)
+        clip = CLIP(knowledge_base=KB, path_of_embeddings=path_of_embeddings,
+                    refinement_operator=op, predictor_name=args.predictor_name,
+                    load_pretrained=args.load_pretrained,
+                    pretrained_predictor_name=args.pretrained_predictor_name,
+                    max_runtime=args.max_runtime, num_workers=args.num_workers)
+        clip.train(training_data, epochs=args.epochs, learning_rate=args.lr,
+                   storage_path=args.storage_path)
+    else:
+        print("Knowledge base or embeddings not found. Please check the paths.")
+
+
+if __name__ == '__main__':
+    set_seed(42)
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--kb', type=str, default=None, help='Paths of a knowledge base (OWL file)')
+    parser.add_argument('--path_train_data', type=str, default=None, help='Path to training data')
+    parser.add_argument('--path_of_embeddings', type=str, default=None,
+                        help='Path to CLIP-compatible embeddings')
+    parser.add_argument('--epochs', type=int, default=5, help='Number of training epochs')
+    parser.add_argument('--lr', type=float, default=0.001,
+                        help='Learning rate for training. The optimizer is Adam.')
+    parser.add_argument('--predictor_name', type=str, default=None,
+                        help='Name of the length predictor to be used. The options are: SetTransformer, GRU, LSTM')
+    parser.add_argument('--load_pretrained', type=str2bool, default=True,
+                        help='Whether to load the pretrained model')
+    parser.add_argument('--pretrained_predictor_name', type=str, default='SetTransformer',
+                        help='Name of the pretrained length predictor to be used. The options are: SetTransformer, GRU, LSTM, CNN')
+    parser.add_argument('--storage_path', type=str, default=None,
+                        help='Path to save the trained models')
+    parser.add_argument('--max_runtime', type=int, default=60,
+                        help='Maximum runtime in for clip')
+    parser.add_argument('--num_workers', type=int, default=0,
+                        help='Number of workers for data loading')
+
+    args = parser.parse_args()
+    # make training data
+    # data_generator = LPGen(kb_path= args.kb, storage_path=args.storage_path)
+    # data_generator.generate()
+    args.tmax = min(args.max_runtime, args.epochs)
+    start(args)

ontolearn/base_nces.py

@@ -38,10 +38,10 @@
 
 class BaseNCES:
 
-    def __init__(self, knowledge_base_path, nces2_or_roces, quality_func, num_predictions, auto_train=True, proj_dim=128, drop_prob=0.1, num_heads=4, num_seeds=1, m=32, ln=False, learning_rate=1e-4, tmax=20, eta_min=1e-5, clip_value=5.0,
+    def __init__(self, knowledge_base, nces2_or_roces, quality_func, num_predictions, auto_train=True, proj_dim=128, drop_prob=0.1, num_heads=4, num_seeds=1, m=32, ln=False, learning_rate=1e-4, tmax=20, eta_min=1e-5, clip_value=5.0,
                  batch_size=256, num_workers=4, max_length=48, load_pretrained=True, verbose: int = 0):
         self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        kb = KnowledgeBase(path=knowledge_base_path)
+        kb = knowledge_base#KnowledgeBase(path=knowledge_base_path)
         self.kb_namespace = list(kb.ontology.classes_in_signature())[0].iri.get_namespace()
         self.dl_parser = DLSyntaxParser(self.kb_namespace)
         self.renderer = DLSyntaxObjectRenderer()
@@ -55,7 +55,7 @@ def __init__(self, knowledge_base_path, nces2_or_roces, quality_func, num_predic
             vocab.extend(concrete_role_names)
             vocab.extend(['⁻', '≤', '≥', 'True', 'False', 'true', 'false', '{', '}', ':', '[', ']', 'double', 'integer', 'date', 'xsd'])
         vocab = sorted(set(vocab)) + ['PAD']
-        self.knowledge_base_path = knowledge_base_path
+        #self.knowledge_base_path = knowledge_base_path
         self.kb = kb
         self.all_individuals = set([ind.str.split("/")[-1] for ind in kb.individuals()])
         self.inv_vocab = np.array(vocab, dtype='object')