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)

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()

refresh()

collate_batch(batch)

collate_batch_inference(batch)

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

predict_length(models, x_pos, x_neg)

fit(*args, **kwargs)

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)

Train RL agent on learning problems.

Returns:

self.