DRILL cannot access classes_in_signature when we use TripleStore() to create the knowledge base

[Quannz]

DRILL crashes when we use kb = TripleStore('http://131.234.178.88:3030/Carcinogenesis/sparql'):

Traceback (most recent call last): File "/local/upb/users/q/quannian/profiles/unix/cs/Pycharm_Project/Ontolearn-0.7.3/Ontolearn/examples/concept_learning_drill_train.py", line 126, in start(parser.parse_args()) File "/local/upb/users/q/quannian/profiles/unix/cs/Pycharm_Project/Ontolearn-0.7.3/Ontolearn/examples/concept_learning_drill_train.py", line 51, in start drill.train(num_of_target_concepts=args.num_of_target_concepts, File "/upb/users/q/quannian/profiles/unix/cs/.conda/envs/ontolearn-0.7.3/lib/python3.10/site-packages/ontolearn/learners/drill.py", line 258, in train training_data = self.generate_learning_problems(num_of_target_concepts, File "/upb/users/q/quannian/profiles/unix/cs/.conda/envs/ontolearn-0.7.3/lib/python3.10/site-packages/ontolearn/learners/drill.py", line 736, in generate_learning_problems for i in self.kb.get_concepts(): File "/upb/users/q/quannian/profiles/unix/cs/.conda/envs/ontolearn-0.7.3/lib/python3.10/site-packages/ontolearn/triple_store.py", line 1177, in get_concepts yield from self.reasoner.classes_in_signature() AttributeError: 'str' object has no attribute 'classes_in_signature'

[Demirrr]

Could you give me the full code used to reproduce this error ?

[Quannz]

import json from argparse import ArgumentParser from ontolearn.triple_store import TripleStoreKnowledgeBase from ontolearn.triple_store import TripleStore import numpy as np from sklearn.model_selection import StratifiedKFold from ontolearn.utils.static_funcs import compute_f1_score from ontolearn.knowledge_base import KnowledgeBase from ontolearn.learning_problem import PosNegLPStandard from ontolearn.refinement_operators import LengthBasedRefinement from ontolearn.learners import Drill from ontolearn.metrics import F1 from ontolearn.heuristics import CeloeBasedReward from owlapy.owl_individual import OWLNamedIndividual, IRI from owlapy.render import DLSyntaxObjectRenderer

def start(args): kb = TripleStore('http://131.234.178.88:3030/Carcinogenesis/sparql')

kb = KnowledgeBase(path=args.path_knowledge_base)

drill = Drill(knowledge_base=kb,
              path_embeddings=args.path_embeddings,
              refinement_operator=LengthBasedRefinement(knowledge_base=kb),
              quality_func=F1(),
              reward_func=CeloeBasedReward(),
              epsilon_decay=args.epsilon_decay,
              learning_rate=args.learning_rate,
              num_of_sequential_actions=args.num_of_sequential_actions,
              num_episode=args.num_episode,
              iter_bound=args.iter_bound,
              max_runtime=args.max_runtime)

if args.path_pretrained_dir:
    drill.load(directory=args.path_pretrained_dir)
else:

    drill.train(num_of_target_concepts=args.num_of_target_concepts,
                num_learning_problems=args.num_of_training_learning_problems)
    drill.save(directory="pretrained_drill")

with open(args.path_learning_problem) as json_file:
    examples = json.load(json_file)
    print(examples)
p = examples['problems']['NOTKNOWN']['positive_examples']
n = examples['problems']['NOTKNOWN']['negative_examples']
print(p)
print(n)
kf = StratifiedKFold(n_splits=args.folds, shuffle=True, random_state=args.random_seed)
X = np.array(p + n)
Y = np.array([1.0 for _ in p] + [0.0 for _ in n])
dl_render = DLSyntaxObjectRenderer()
for (ith, (train_index, test_index)) in enumerate(kf.split(X, Y)):
    train_pos = {pos_individual for pos_individual in X[train_index][Y[train_index] == 1]}
    train_neg = {neg_individual for neg_individual in X[train_index][Y[train_index] == 0]}
    test_pos = {pos_individual for pos_individual in X[test_index][Y[test_index] == 1]}
    test_neg = {neg_individual for neg_individual in X[test_index][Y[test_index] == 0]}
    train_lp = PosNegLPStandard(pos=set(map(OWLNamedIndividual, map(IRI.create, train_pos))),
                                neg=set(map(OWLNamedIndividual, map(IRI.create, train_neg))))

    test_lp = PosNegLPStandard(pos=set(map(OWLNamedIndividual, map(IRI.create, test_pos))),
                               neg=set(map(OWLNamedIndividual, map(IRI.create, test_neg))))

    pred_drill = drill.fit(train_lp).best_hypotheses()
    train_f1_drill = compute_f1_score(individuals=frozenset({i for i in kb.individuals(pred_drill)}),
                                      pos=train_lp.pos,
                                      neg=train_lp.neg)
    # () Quality on test data
    test_f1_drill = compute_f1_score(individuals=frozenset({i for i in kb.individuals(pred_drill)}),
                                     pos=test_lp.pos,
                                     neg=test_lp.neg)
    print(
        f"Prediction: {dl_render.render(pred_drill)} | Train Quality: {train_f1_drill:.3f} | Test Quality: {test_f1_drill:.3f} \n")

if name == 'main': parser = ArgumentParser()

General

parser.add_argument("--path_knowledge_base", type=str,
                    default="/upb/users/q/quannian/profiles/unix/cs/KG/Carcinogenesis/carcinogenesis.owl")
parser.add_argument("--path_embeddings", type=str,
                    default='/upb/users/q/quannian/profiles/unix/cs/Pycharm_Project/Dice-Embeddings-CSV/Shallom_Carcinogenesis/Shallom_entity_embeddings.csv')
parser.add_argument("--num_of_target_concepts",
                    type=int,
                    default=1)
parser.add_argument("--num_of_training_learning_problems",
                    type=int,
                    default=1)
parser.add_argument("--path_pretrained_dir", type=str, default=None)

parser.add_argument("--path_learning_problem", type=str, default='/upb/users/q/quannian/profiles/unix/cs/Pycharm_Project/Ontolearn-0.7.3/Ontolearn/LPs/Carcinogenesis/lps.json',
                    help="Path to a .json file that contains 2 properties 'positive_examples' and "
                         "'negative_examples'. Each of this properties should contain the IRIs of the respective"
                         "instances. e.g. 'some/path/lp.json'")
parser.add_argument("--max_runtime", type=int, default=60, help="Max runtime")
parser.add_argument("--folds", type=int, default=10, help="Number of folds of cross validation.")
parser.add_argument("--random_seed", type=int, default=1)
parser.add_argument("--iter_bound", type=int, default=10_000, help='iter_bound during testing.')
# DQL related
parser.add_argument("--num_episode", type=int, default=1, help='Number of trajectories created for a given lp.')

parser.add_argument("--epsilon_decay", type=float, default=.01, help='Epsilon greedy trade off per epoch')
parser.add_argument("--max_len_replay_memory", type=int, default=1024,
                    help='Maximum size of the experience replay')
parser.add_argument("--num_epochs_per_replay", type=int, default=2,
                    help='Number of epochs on experience replay memory')
parser.add_argument('--num_of_sequential_actions', type=int, default=1, help='Length of the trajectory.')

# NN related
parser.add_argument("--learning_rate", type=int, default=.01)

[Demirrr]

You are passing a reasoner to TripleStore :)

kb=TripleStore(url="http://131.234.178.88:3030/Carcinogenesis/sparql")
for i in kb.classes_in_signature():
    print(i)

[Quannz]

Oh I see thanks :)