ontolearn.learners.tree_learner

Classes

TDL	Tree-based Description Logic Concept Learner

Functions

explain_inference(clf, X)	Given a trained Decision Tree, extract the paths from root to leaf nodes for each entities
concepts_reducer(...)	Reduces a list of OWLClassExpression instances into a single instance of OWLObjectUnionOf or OWLObjectIntersectionOf

Module Contents

ontolearn.learners.tree_learner.explain_inference(clf, X: pandas.DataFrame)

Given a trained Decision Tree, extract the paths from root to leaf nodes for each entities https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html#understanding-the-decision-tree-structure

ontolearn.learners.tree_learner.concepts_reducer(concepts: List[owlapy.class_expression.OWLClassExpression], reduced_cls: Callable) → owlapy.class_expression.OWLObjectUnionOf | owlapy.class_expression.OWLObjectIntersectionOf

Reduces a list of OWLClassExpression instances into a single instance of OWLObjectUnionOf or OWLObjectIntersectionOf

class ontolearn.learners.tree_learner.TDL(knowledge_base, use_inverse: bool = False, use_data_properties: bool = False, use_nominals: bool = False, use_card_restrictions: bool = False, kwargs_classifier: dict = None, max_runtime: int = 1, grid_search_over: dict = None, grid_search_apply: bool = False, report_classification: bool = True, plot_tree: bool = False, plot_embeddings: bool = False, plot_feature_importance: bool = False, verbose: int = 10)

Tree-based Description Logic Concept Learner

use_nominals

use_card_restrictions

grid_search_over

knowledge_base

report_classification

plot_tree

plot_embeddings

plot_feature_importance

clf = None

kwargs_classifier

max_runtime

features = None

disjunction_of_conjunctive_concepts = None

conjunctive_concepts = None

owl_class_expressions

cbd_mapping: Dict[str, Set[Tuple[str, str]]]

types_of_individuals

verbose

data_property_cast

X = None

y = None

extract_expressions_from_owl_individuals(individuals: List[owlapy.owl_individual.OWLNamedIndividual]) → Tuple[Dict[str, owlapy.class_expression.OWLClassExpression], Dict[str, str]]

construct_sparse_binary_representations(features: List[owlapy.class_expression.OWLClassExpression], examples: List[owlapy.owl_individual.OWLNamedIndividual], examples_to_features) → numpy.array

create_training_data(learning_problem: PosNegLPStandard) → Tuple[pandas.DataFrame, pandas.DataFrame]

construct_owl_expression_from_tree(X: pandas.DataFrame, y: pandas.DataFrame) → List[owlapy.class_expression.OWLObjectIntersectionOf]

Construct an OWL class expression from a decision tree

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. (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

property classification_report: str

best_hypotheses(n=1) → Tuple[owlapy.class_expression.OWLClassExpression, List[owlapy.class_expression.OWLClassExpression]]

Return the prediction

abstract predict(X: List[owlapy.owl_individual.OWLNamedIndividual], proba=True) → numpy.ndarray

Predict the likelihoods of individuals belonging to the classes