ontolearn.learners.clip

CLIP: Concept Learner with Integrated Length Prediction.

Classes

CLIP

Concept Learner with Integrated Length Prediction.

Module Contents

class ontolearn.learners.clip.CLIP(knowledge_base: AbstractKnowledgeBase, reasoner: owlapy.abstracts.AbstractOWLReasoner | None = None, refinement_operator: BaseRefinement[OENode] | None = ExpressRefinement, quality_func: AbstractScorer | None = None, heuristic_func: AbstractHeuristic | None = None, terminate_on_goal: bool | None = None, iter_bound: int | None = None, max_num_of_concepts_tested: int | None = None, max_runtime: int | None = None, max_results: int = 10, best_only: bool = False, calculate_min_max: bool = True, path_of_embeddings='', predictor_name=None, pretrained_predictor_name=['SetTransformer', 'LSTM', 'GRU', 'CNN'], load_pretrained=False, num_workers=4, num_examples=1000, output_size=15)[source]

Bases: ontolearn.learners.CELOE

Concept Learner with Integrated Length Prediction. This algorithm extends the CELOE algorithm by using concept length predictors and a different refinement operator, i.e., ExpressRefinement

best_descriptions

Best hypotheses ordered.

Type:: EvaluatedDescriptionSet[OENode, QualityOrderedNode]

best_only

If False pick only nodes with quality < 1.0, else pick without quality restrictions.

Type:: bool

calculate_min_max

Calculate minimum and maximum horizontal expansion? Statistical purpose only.

Type:: bool

heuristic_func

Function to guide the search heuristic.

Type:: AbstractHeuristic

heuristic_queue

A sorted set that compares the nodes based on Heuristic.

Type:: SortedSet[OENode]

iter_bound

Limit to stop the algorithm after n refinement steps are done.

Type:: int

kb

The knowledge base that the concept learner is using.

Type:: AbstractKnowledgeBase

max_child_length

Limit the length of concepts generated by the refinement operator.

Type:: int

max_he

Maximal value of horizontal expansion.

Type:: int

max_num_of_concepts_tested

Type:: int

max_runtime

Limit to stop the algorithm after n seconds.

Type:: int

min_he

Minimal value of horizontal expansion.

Type:: int

name

Name of the model = ‘celoe_python’.

Type:: str

_number_of_tested_concepts

Yes, you got it. This stores the number of tested concepts.

Type:: int

operator

Operator used to generate refinements.

Type:: BaseRefinement

quality_func

Type:: AbstractScorer

reasoner

The reasoner that this model is using.

Type:: AbstractOWLReasoner

search_tree

Dict to store the TreeNode for a class expression.

Type:: Dict[OWLClassExpression, TreeNode[OENode]]

start_class

The starting class expression for the refinement operation.

Type:: OWLClassExpression

start_time

The time when fit() starts the execution. Used to calculate the total time fit() takes to execute.

Type:: float

terminate_on_goal

Whether to stop the algorithm if a perfect solution is found.

Type:: bool

__slots__ = ('best_descriptions', 'max_he', 'min_he', 'best_only', 'calculate_min_max', 'heuristic_queue',...

name = 'CLIP'

predictor_name = None

pretrained_predictor_name = ['SetTransformer', 'LSTM', 'GRU', 'CNN']

knowledge_base

load_pretrained = False

num_workers = 4

output_size = 15

num_examples = 1000

path_of_embeddings = ''

device

length_predictor

get_length_predictor()[source]

refresh()[source]

collate_batch(batch)[source]

collate_batch_inference(batch)[source]

pos_neg_to_tensor(pos: List[owlapy.owl_individual.OWLNamedIndividual] | List[str], neg: List[owlapy.owl_individual.OWLNamedIndividual] | List[str])[source]

predict_length(models, x_pos, x_neg)[source]

fit(*args, **kwargs)[source]: Find hypotheses that explain pos and neg.

train(data: Iterable[List[Tuple]], epochs=300, batch_size=256, learning_rate=0.001, decay_rate=0.0, clip_value=5.0, save_model=True, storage_path=None, optimizer='Adam', record_runtime=True, example_sizes=None, shuffle_examples=False)[source]

Optional training hook; a no-op by default.

Training is learner-specific: learners that require training (e.g. Drill, NCES, NCES2, ROCES, CLIP) override this method with their own signatures, while purely symbolic learners (e.g. CELOE, OCEL) need no training. This default exists so that untrainable learners can be used in generic pipelines that call train() uniformly (e.g. ontolearn.experiments.Experiments.carry_out_kfold_cross_validation()).

Returns:: self.