ontolearn.concept_learner

Concept learning algorithms of Ontolearn.

Classes

`EvoLearner`	An evolutionary approach to learn concepts in ALCQ(D).
`CLIP`	Concept Learner with Integrated Length Prediction.
`NCES`	Neural Class Expression Synthesis.

Module Contents

class ontolearn.concept_learner.EvoLearner(knowledge_base: KnowledgeBase, reasoner: owlapy.abstracts.AbstractOWLReasoner | None = None, quality_func: AbstractScorer | None = None, fitness_func: AbstractFitness | None = None, init_method: AbstractEAInitialization | None = None, algorithm: AbstractEvolutionaryAlgorithm | None = None, mut_uniform_gen: AbstractEAInitialization | None = None, value_splitter: AbstractValueSplitter | None = None, terminate_on_goal: bool | None = None, max_runtime: int | None = None, use_data_properties: bool = True, use_card_restrictions: bool = True, use_inverse: bool = False, tournament_size: int = 7, card_limit: int = 10, population_size: int = 800, num_generations: int = 200, height_limit: int = 17)[source]

Bases: ontolearn.base_concept_learner.BaseConceptLearner

An evolutionary approach to learn concepts in ALCQ(D).

algorithm

The evolutionary algorithm.

Type:: AbstractEvolutionaryAlgorithm

card_limit

The upper cardinality limit if using cardinality restriction on object properties.

Type:: int

fitness_func

Fitness function.

Type:: AbstractFitness

height_limit

The maximum value allowed for the height of the Crossover and Mutation operations.

Type:: int

init_method

The evolutionary algorithm initialization method.

Type:: AbstractEAInitialization

kb

The knowledge base that the concept learner is using.

Type:: KnowledgeBase

max_num_of_concepts_tested

Limit to stop the algorithm after n concepts tested.

Type:: int

max_runtime

max_runtime: Limit to stop the algorithm after n seconds.

Type:: int

mut_uniform_gen

The initialization method to create the tree for mutation operation.

Type:: AbstractEAInitialization

name

Name of the model = ‘evolearner’.

Type:: str

num_generations

Number of generation for the evolutionary algorithm.

Type:: int

_number_of_tested_concepts

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

Type:: int

population_size

Population size for the evolutionary algorithm.

Type:: int

pset

Contains the primitives that can be used to solve a Strongly Typed GP problem.

Type:: gp.PrimitiveSetTyped

quality_func: Function to evaluate the quality of solution concepts.

reasoner

The reasoner that this model is using.

Type:: AbstractOWLReasoner

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

toolbox

A toolbox for evolution that contains the evolutionary operators.

Type:: base.Toolbox

tournament_size

The number of evolutionary individuals participating in each tournament.

Type:: int

use_card_restrictions

Use cardinality restriction for object properties?

Type:: bool

use_data_properties

Consider data properties?

Type:: bool

use_inverse

Consider inversed concepts?

Type:: bool

value_splitter

Used to calculate the splits for data properties values.

Type:: AbstractValueSplitter

__slots__ = ('fitness_func', 'init_method', 'algorithm', 'value_splitter', 'tournament_size',...

name = 'evolearner'

kb: KnowledgeBase

fitness_func: AbstractFitness

init_method: AbstractEAInitialization

algorithm: AbstractEvolutionaryAlgorithm

mut_uniform_gen: AbstractEAInitialization

value_splitter: AbstractValueSplitter

use_data_properties: bool

use_card_restrictions: bool

use_inverse: bool

tournament_size: int

card_limit: int

population_size: int

num_generations: int

height_limit: int

pset: deap.gp.PrimitiveSetTyped

toolbox: deap.base.Toolbox

reasoner

total_fits = 0

register_op(alias: str, function: Callable, *args, **kargs)[source]

Register a function in the toolbox under the name alias. You may provide default arguments that will be passed automatically when calling the registered function. Fixed arguments can then be overriden at function call time.

Parameters:

alias – The name the operator will take in the toolbox. If the alias already exist it will overwrite the operator already present.
function – The function to which refer the alias.
args – One or more argument (and keyword argument) to pass automatically to the registered function when called, optional.

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

best_hypotheses(n: int = 1, key: str = 'fitness', return_node: bool = False) → owlapy.class_expression.OWLClassExpression | Iterable[owlapy.class_expression.OWLClassExpression][source]

Get the current best found hypotheses according to the quality.

Parameters:: n – Maximum number of results.
Returns:: Iterable with hypotheses in form of search tree nodes.

clean(partial: bool = False)[source]: Clear all states of the concept learner.

class ontolearn.concept_learner.CLIP(knowledge_base: KnowledgeBase, knowledge_base_path='', 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:: KnowledgeBase

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

pretrained_predictor_name

knowledge_base_path

load_pretrained

num_workers

output_size

num_examples

path_of_embeddings

instance_embeddings

input_size

device

length_predictor

get_length_predictor()[source]

refresh()[source]

collate_batch(batch)[source]

collate_batch_inference(batch)[source]

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

predict_length(models, x1, x2)[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]

Train RL agent on learning problems.

Returns:: self.

class ontolearn.concept_learner.NCES(knowledge_base_path, quality_func: AbstractScorer | None = None, num_predictions=5, learner_names=['SetTransformer'], path_of_embeddings='', proj_dim=128, rnn_n_layers=2, drop_prob=0.1, num_heads=4, num_seeds=1, num_inds=32, ln=False, learning_rate=0.0001, decay_rate=0.0, clip_value=5.0, batch_size=256, num_workers=4, max_length=48, load_pretrained=True, sorted_examples=False, verbose: int = 0)[source]

Bases: ontolearn.base_nces.BaseNCES

Neural Class Expression Synthesis.

quality_func

num_predictions

path_of_embeddings

device

max_length

proj_dim

rnn_n_layers

drop_prob

num_heads

num_seeds

num_inds

ln

load_pretrained

sorted_examples

verbose

model

dl_parser

best_predictions = None

get_synthesizer(path=None)[source]

refresh(path=None)[source]

sample_examples(pos, neg)[source]

get_prediction(models, x1, x2)[source]

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

fit(learning_problem: PosNegLPStandard, **kwargs)[source]

best_hypotheses(n=1) → owlapy.class_expression.OWLClassExpression | Iterable[owlapy.class_expression.OWLClassExpression][source]

convert_to_list_str_from_iterable(data)[source]

fit_from_iterable(dataset: List[Tuple[str, Set[owlapy.owl_individual.OWLNamedIndividual], Set[owlapy.owl_individual.OWLNamedIndividual]]] | List[Tuple[str, Set[str], Set[str]]], shuffle_examples=False, verbose=False, **kwargs) → List[source]

Dataset is a list of tuples where the first items are strings corresponding to target concepts.
This function returns predictions as owl class expressions, not nodes as in fit

static generate_training_data(kb_path, num_lps=1000, storage_dir='./NCES_Training_Data')[source]

train(data: Iterable[List[Tuple]] = None, epochs=50, batch_size=64, num_lps=1000, learning_rate=0.0001, decay_rate=0.0, clip_value=5.0, num_workers=8, save_model=True, storage_path=None, optimizer='Adam', record_runtime=True, example_sizes=None, shuffle_examples=False)[source]