Prob2FOIL: rule learner for probabilistic logic
Project description
ProbFOIL is a probabilistic extension of FOIL that is capable of learning probabilistic rules from probabilistic data.
ProbFOIL 2.1 is a redesign of the Prob2FOIL algorithm that was introduced in https://lirias.kuleuven.be/handle/123456789/499989. It works on top of ProbLog 2.1.
If you are looking for the version used in the paper, you should check out the tag paper_version.
Installation
ProbFOIL 2.1 requires ProbLog 2.1. You can install ProbLog by using the command:
pip install problogProbFOIL does not require any further installation.
Usage
The input of ProbFOIL consists of two parts: settings and data. These are both specified in Prolog (or ProbLog) files, and they can be combined into one.
The data consists of (probabilistic) facts. The settings define
target: the predicate we want to learn
modes: which predicates can be added to the rules
types: type information for the predicates
other settings related to the data
To use:
probfoil data.plor, in the repository version
python probfoil/probfoil.py data.plMultiple files can be specified and the information in them is concatenated. (For example, it is advisable to separate settings from data).
Several command line arguments are available. Use --help to get more information.
Settings format
Target
The target should be specified by adding a fact learn(predicate/arity).
Modes
The modes should be specified by adding facts of the form mode(predicate(mode1, mode2, ...), where modeX is the mode specifier for argument X. Possible mode specifiers are:
+: the variable at this position must already exist when the literal is added
-: the variable at this position does not exist yet in the rule (note that this is stricter than usual)
c: a constant should be introduced here; possible value are derived automatically from the data
Types
For each relevant predicate (target and modes) there should be a type specifier. This specifier is of the form base(predicate(type1, type2, ...), where typeX is a type identifier. Type can be identified by arbitrary Prolog atoms (e.g. person, a, etc.)
Example generation
By default, examples are generated by quering the data for the target predicate. Negative examples can be specified by adding zero-probability facts, e.g.:
0.0::grandmother(john, mary).Alternatively, ProbFOIL can derive negative examples automatically by taking combinations of possible values for the target arguments. Note that this can lead to a combinatorial explosion. To enable this behavior, you can specify the fact
example_mode(auto).Example
% Modes
mode(male(+)).
mode(parent(+,+)).
mode(parent(+,-)).
mode(parent(-,+)).
% Type definitions
base(parent(person,person)).
base(male(person)).
base(female(person)).
base(mother(person,person)).
base(grandmother(person,person)).
base(father(person,person)).
base(male_ancestor(person,person)).
base(female_ancestor(person,person)).
% Target
learn(grandmother/2).
% How to generate negative examples
example_mode(auto).Further examples can be found in the directory examples.
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