Merge pull request #327 from dice-group/DRILL

WIP: DRILL training is available
dice-group · Dec 6, 2023 · 551407e · 551407e
2 parents 76b9a8c + 65aacbd
commit 551407e
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Showing 4 changed files with 202 additions and 145 deletions.
diff --git a/examples/concept_learning_evaluation.py b/examples/concept_learning_evaluation.py
@@ -14,20 +14,15 @@
 
 
 def dl_concept_learning(args):
-    try:
-        os.chdir("examples")
-    except FileNotFoundError:
-        pass
-
     with open(args.lps) as json_file:
         settings = json.load(json_file)
 
     kb = KnowledgeBase(path=args.kb)
-
-    ocel=OCEL(knowledge_base=kb, quality_func=F1(), max_runtime=args.max_runtime)
-    celoe=CELOE(knowledge_base=kb, quality_func=F1(), max_runtime=args.max_runtime)
-    evo=EvoLearner(knowledge_base=kb, quality_func=F1(), max_runtime=args.max_runtime)
-    drill=Drill(knowledge_base=kb, quality_func=F1(), max_runtime=args.max_runtime)
+    drill = Drill(knowledge_base=kb, path_pretrained_kge=args.path_pretrained_kge, quality_func=F1(),
+                  max_runtime=args.max_runtime).train(num_episode=1, num_learning_problems=1)
+    ocel = OCEL(knowledge_base=kb, quality_func=F1(), max_runtime=args.max_runtime)
+    celoe = CELOE(knowledge_base=kb, quality_func=F1(), max_runtime=args.max_runtime)
+    evo = EvoLearner(knowledge_base=kb, quality_func=F1(), max_runtime=args.max_runtime)
     columns = ["LP",
                "OCEL", "F1-OCEL", "RT-OCEL",
                "CELOE", "F1-CELOE", "RT-CELOE",
@@ -45,11 +40,9 @@ def dl_concept_learning(args):
         lp = PosNegLPStandard(pos=typed_pos, neg=typed_neg)
 
         start_time = time.time()
-        # Untrained & max runtime is not fully integrated.
         pred_drill = drill.fit(lp).best_hypotheses(n=1)
         rt_drill = time.time() - start_time
 
-
         start_time = time.time()
         pred_ocel = ocel.fit(lp).best_hypotheses(n=1)
         rt_ocel = time.time() - start_time
@@ -80,6 +73,5 @@ def dl_concept_learning(args):
     parser.add_argument("--max_runtime", type=int, default=10)
     parser.add_argument("--lps", type=str, default="synthetic_problems.json")
     parser.add_argument("--kb", type=str, default="../KGs/Family/family-benchmark_rich_background.owl")
-    parser.add_argument("--path_pretrained_kge", type=str, default="../KeciFamilyRun")
-
+    parser.add_argument("--path_pretrained_kge", type=str, default=None)
     dl_concept_learning(parser.parse_args())
diff --git a/ontolearn/data_struct.py b/ontolearn/data_struct.py
@@ -12,8 +12,6 @@ def __init__(self, current_state: torch.FloatTensor, next_state_batch: torch.Flo
                  n: torch.FloatTensor):
         assert len(p) > 0 and len(n) > 0
         num_next_states = len(next_state_batch)
-
-
         current_state = current_state.repeat(num_next_states, 1, 1)
         p = p.repeat((num_next_states, 1, 1))
         n = n.repeat((num_next_states, 1, 1))
@@ -63,8 +61,9 @@ def __init__(self, current_state_batch: torch.Tensor, next_state_batch: torch.Te
         assert self.S.shape == self.S_Prime.shape == self.Positives.shape == self.Negatives.shape
         assert self.S.dtype == self.S_Prime.dtype == self.Positives.dtype == self.Negatives.dtype == torch.float32
         self.X = torch.cat([self.S, self.S_Prime, self.Positives, self.Negatives], 1)
+
         num_points, depth, dim = self.X.shape
-        self.X = self.X.view(num_points, depth, 1, dim)
+        # self.X = self.X.view(num_points, depth, 1, dim)
         # X[0] => corresponds to a data point, X[0] \in R^{4 \times 1 \times dim}
         # where X[0][0] => current state representation R^{1 \times dim}
         # where X[0][1] => next state representation R^{1 \times dim}