Drill family example doesn't work while using Tentris.

[Quannz]

I tried to test drill with Tentris on family dataset:
when I load the KG with TripleStore

kb = TripleStore(url="http://localhost:9010/sparql")

here is the error I faced:

(ontolearn0.8.0) quannian@eml4u:~/Drill/Ontolearn-0.7.3/Ontolearn/examples/Drill_family$ python Drill_Family.py

Goal Concept:    Daughter       E^+:[3]  E^-:[3]
Avg loss: 45.76734

Prediction: Female ⊓ (∀ hasChild⁻.(¬Brother)) | Train Quality: 0.828 | Test Quality: 0.909

Traceback (most recent call last):
  File "/local/upb/users/q/quannian/profiles/unix/cs/Drill/Ontolearn-0.7.3/Ontolearn/examples/Drill_family/Drill_Family.py", line 119, in <module>
    start(parser.parse_args())
  File "/local/upb/users/q/quannian/profiles/unix/cs/Drill/Ontolearn-0.7.3/Ontolearn/examples/Drill_family/Drill_Family.py", line 71, in start
    pred_drill = drill.fit(train_lp).best_hypotheses()
  File "/upb/users/q/quannian/profiles/unix/cs/.conda/envs/ontolearn0.8.0/lib/python3.10/site-packages/ontolearn/learners/drill.py", line 377, in fit
    preds = self.predict_values(current_state=most_promising,
  File "/upb/users/q/quannian/profiles/unix/cs/.conda/envs/ontolearn0.8.0/lib/python3.10/site-packages/ontolearn/learners/drill.py", line 661, in predict_values
    next_state_batch.append(self.get_embeddings_individuals(self.get_individuals(_)))
  File "/upb/users/q/quannian/profiles/unix/cs/.conda/envs/ontolearn0.8.0/lib/python3.10/site-packages/ontolearn/learners/drill.py", line 582, in get_embeddings_individuals
    emb = torch.mean(torch.from_numpy(self.df_embeddings.loc[individuals].values, ), dim=0)
  File "/upb/users/q/quannian/profiles/unix/cs/.conda/envs/ontolearn0.8.0/lib/python3.10/site-packages/pandas/core/indexing.py", line 1191, in __getitem__
    return self._getitem_axis(maybe_callable, axis=axis)
  File "/upb/users/q/quannian/profiles/unix/cs/.conda/envs/ontolearn0.8.0/lib/python3.10/site-packages/pandas/core/indexing.py", line 1420, in _getitem_axis
    return self._getitem_iterable(key, axis=axis)
  File "/upb/users/q/quannian/profiles/unix/cs/.conda/envs/ontolearn0.8.0/lib/python3.10/site-packages/pandas/core/indexing.py", line 1360, in _getitem_iterable
    keyarr, indexer = self._get_listlike_indexer(key, axis)
  File "/upb/users/q/quannian/profiles/unix/cs/.conda/envs/ontolearn0.8.0/lib/python3.10/site-packages/pandas/core/indexing.py", line 1558, in _get_listlike_indexer
    keyarr, indexer = ax._get_indexer_strict(key, axis_name)
  File "/upb/users/q/quannian/profiles/unix/cs/.conda/envs/ontolearn0.8.0/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
    self._raise_if_missing(keyarr, indexer, axis_name)
  File "/upb/users/q/quannian/profiles/unix/cs/.conda/envs/ontolearn0.8.0/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6252, in _raise_if_missing
    raise KeyError(f"{not_found} not in index")
KeyError: "['http://www.benchmark.org/family'] not in index"

BUT when I used the KnowledgeBase to load the KG:

kb = KnowledgeBase(path="/upb/users/q/quannian/profiles/unix/cs/Drill/Ontolearn-0.7.3/Ontolearn/KGs/Family/family-benchmark_rich_background.nt")

It worked without error.

Here is the code:

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(url="http://localhost:9010/sparql")
    # kb = KnowledgeBase(path="/upb/users/q/quannian/profiles/unix/cs/Drill/Ontolearn-0.7.3/Ontolearn/KGs/Family/family-benchmark_rich_background.nt")
    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)
    p = examples['problems']['Aunt']['positive_examples']
    n = examples['problems']['Aunt']['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="")
    parser.add_argument("--path_embeddings", type=str,
                        default='/upb/users/q/quannian/profiles/unix/cs/Embedding/embeddings/ConEx_Family/ConEx_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/LPs/LPs/Family/lp_dl_learner.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)

    start(parser.parse_args())