ontolearn.learners.tree_learner_refinement_inherit

Attributes

logger

Classes

TDL_refinement

Tree-based Description Logic Concept Learner

Module Contents

ontolearn.learners.tree_learner_refinement_inherit.logger

class ontolearn.learners.tree_learner_refinement_inherit.TDL_refinement(knowledge_base, use_inverse: bool = False, use_data_properties_numeric: bool = True, use_data_properties_boolean: bool = True, use_data_properties_string: bool = True, use_data_properties_date: bool = True, use_nominals: bool = False, use_card_restrictions: bool = True, kwargs_classifier: dict = None, max_runtime: int = None, grid_search_over: dict = None, grid_search_apply: bool = False, kwargs_grid_search: dict = None, report_classification: bool = False, plot_tree: bool = False, plot_embeddings: bool = False, plot_feature_importance: bool = False, verbose: int = 10, verbalize: bool = False, feature_refinement: bool = True, plot_importance_evo: bool = False, refine_iterations: int = 3, topk: int = 30)

Bases: ontolearn.learners.TDL

Tree-based Description Logic Concept Learner

feature_refinement = True

plot_importance_evo = False

refine_iterations = 3

topk = 30

concept_per_iteration: List = []

top_feature_dicts: List = []

initial_importance_dict: Dict

use_data_properties

data_property_datatype_dict: Dict[owlapy.owl_property.OWLDataProperty, owlapy.owl_datatype.OWLDatatype]

extract_expressions_from_owl_individuals(individuals: List[owlapy.owl_individual.OWLNamedIndividual], start_time: float = None) → Tuple[numpy.ndarray, List[owlapy.class_expression.OWLClassExpression]]

plot_perm_feature_importances(perm_importance_result, feat_name)

bar plot the permutation feature importance

plot_shap_feature_importances(shap_vals, feat_name)

Bar plot the mean absolute SHAP feature importance

refine_numerical_features(topk_expressions: list[owlapy.class_expression.OWLClassExpression], iteration: int)

Refine numerical features based on data property values and fitted distributions.

plot_importance_evolution(initial_dict, refined_dicts, top_k=10)

fit(learning_problem: PosNegLPStandard = None, max_runtime: int = None)

Fit the learner to the given learning problem

1. Extract multi-hop information about E^+ and E^-.

2. Create OWL Class Expressions from (1)

3. Build a binary sparse training data X where first |E+| rows denote the binary representations of positives Remaining rows denote the binary representations of E⁻

4. Create binary labels.

5. Train a decision tree classifier on (3) and (4)

(6) If feature refinement is enabled, refine the numerical dp features based on SHAP values and data property ranges. (4) Construct a set of DL concept for each e in E^+ (5) Union (4)

Parameters:

learning_problem – The learning problem

:param max_runtime:total runtime of the learning