srlearn.Background

class srlearn.Background(*, modes=None, ok_if_unknown=None, bridgers=None, ranges=None, number_of_clauses=100, number_of_cycles=100, recursion=False, line_search=False, use_std_logic_variables=False, use_prolog_variables=True, load_all_libraries=False, load_all_basic_modes=False)[source]

Background Knowledge for a database.

Background knowledge expressed in the form of modes.

__init__(*, modes=None, ok_if_unknown=None, bridgers=None, ranges=None, number_of_clauses=100, number_of_cycles=100, recursion=False, line_search=False, use_std_logic_variables=False, use_prolog_variables=True, load_all_libraries=False, load_all_basic_modes=False)[source]

Initialize a set of background knowledge

Parameters
modeslist of str (default: None)

Modes constrain the search space for hypotheses.

ok_if_unknownlist of str (default: None)

Okay if not known.

bridgerslist of str (default: None)

List of bridger predicates.

rangesdict of str (default: None)

Dict mapping object types to discrete categories

number_of_clausesint, optional (default: 100)

Maximum number of clauses in the tree (i.e. maximum number of leaves)

number_of_cyclesint, optional (default: 100)

Maximum number of times the code will loop to learn clauses, increments even if no new clauses are learned.

line_searchbool, optional (default: False)

Use lineSearch

recursionbool, optional (default: False)

Use recursion

use_std_logic_variablesbool, optional (default: False)

Set the stdLogicVariables parameter to True

use_prolog_variablesbool, optional (default: True)

Set the usePrologVariables parameter to True

load_all_librariesbool, optional (default: False)

Load libraries: arithmeticInLogic, comparisonInLogic, differentInLogic, listsInLogic

load_all_basic_modesbool, optional (default: False)

Load modes_arithmeticInLogic, modes_comparisonInLogic, modes_differentInLogic, modes_listsInLogic These may require many cycles while proving.

Notes

Descriptions of these parameters are lifted almost word-for-word from the BoostSRL-Wiki “Advanced Parameters” page [1].

Some of these parameters are defined in multiple places. This is mostly to follow the sklearn-style requirement for all tune-able parameters to be part of the object while still being relatively similar to the style where BoostSRL has parameters defined in a modes file.

1

https://starling.utdallas.edu/software/boostsrl/wiki/advanced-parameters/

Examples

This demonstrates how to add parameters to the Background object. The main thing to take note of is the modes parameter, where background knowledge of the Toy-Cancer domain is specified.

>>> from srlearn import Background
>>> bk = Background(
...     modes=[
...         "cancer(+Person).",
...         "smokes(+Person).",
...         "friends(+Person,-Person).",
...         "friends(-Person,+Person).",
...     ],
... )
>>> print(bk)
setParam: numOfClauses=100.
setParam: numOfCycles=100.
usePrologVariables: true.
setParam: nodeSize=2.
setParam: maxTreeDepth=3.
mode: cancer(+Person).
mode: smokes(+Person).
mode: friends(+Person,-Person).
mode: friends(-Person,+Person).

This Background object is used by the srlearn.rdn.BoostedRDN class to write the parameters to a background.txt file before running BoostSRL.

>>> from srlearn import Background
>>> from srlearn.datasets import load_toy_cancer
>>> train, _ = load_toy_cancer()
>>> bk = Background(modes=train.modes)
>>> bk.write("training/")

Examples using srlearn.Background

Estimating Feature Importance

Estimating Feature Importance

Estimating Feature Importance
Smokes-Friends-Cancer

Smokes-Friends-Cancer

Smokes-Friends-Cancer
Family Relationships Domain

Family Relationships Domain

Family Relationships Domain