py3plex.algorithms.hedwig.learners package¶

Submodules¶

py3plex.algorithms.hedwig.learners.bottomup module¶

Main learner class.

class py3plex.algorithms.hedwig.learners.bottomup.BottomUpLearner(kb, n=None, min_sup=1, sim=1, depth=4, target=None, use_negations=False)¶

Bases: object

Bottom-up learner.

Default = 'default'¶

Improvement = 'improvement'¶

Similarity = 'similarity'¶

bottom_clause()¶

get_subclasses(pred)¶

get_superclasses(pred)¶

induce()¶: Induces rules for the given knowledge base.

is_implicit_root(pred)¶

py3plex.algorithms.hedwig.learners.learner module¶

Main learner class.

@author: anze.vavpetic@ijs.si

class py3plex.algorithms.hedwig.learners.learner.Learner(kb, n=None, min_sup=1, sim=1, depth=4, target=None, use_negations=False, optimal_subclass=False)¶

Bases: object

Learner class, supporting various types of induction from the knowledge base.

Default = 'default'¶

Improvement = 'improvement'¶

Similarity = 'similarity'¶

can_specialize(rule)¶: Is the rule good enough to be further refined?

extend(rules, specializations)¶: Extends the ruleset in the given way.

extend_replace_worst(rules, specializations)¶: Extends the list by replacing the worst rules.

extend_with_similarity(rules, specializations)¶: Extends the list based on how similar is ‘new_rule’ to the rules contained in ‘rules’.

get_subclasses(pred)¶

get_superclasses(pred)¶

group_score(rules)¶: Calculates the score of the whole list of rules.

induce()¶: Induces rules for the given knowledge base.

is_implicit_root(pred)¶

non_redundant(rule, new_rule)¶: Is the rule non-redundant compared to its immediate generalization?

specialize(rule)¶: Returns a list of all specializations of ‘rule’.

specialize_add_relation(rule)¶: Specialize with new binary relation.

py3plex.algorithms.hedwig.learners.optimal module¶

Main learner class.

@author: anze.vavpetic@ijs.si

class py3plex.algorithms.hedwig.learners.optimal.OptimalLearner(kb, n=None, min_sup=1, sim=1, depth=4, target=None, use_negations=False, optimal_subclass=True)¶

Bases: py3plex.algorithms.hedwig.learners.learner.Learner

Finds the optimal top-k rules.

induce()¶: Induces rules for the given knowledge base.

Module contents¶

py3plex.algorithms.hedwig.learners.HeuristicLearner¶: alias of py3plex.algorithms.hedwig.learners.learner.Learner

class py3plex.algorithms.hedwig.learners.OptimalLearner(kb, n=None, min_sup=1, sim=1, depth=4, target=None, use_negations=False, optimal_subclass=True)¶

Bases: py3plex.algorithms.hedwig.learners.learner.Learner

Finds the optimal top-k rules.

induce()¶: Induces rules for the given knowledge base.