This module performs learning over Logic Programs with Annotated Disjunctions and CP-Logic programs. It performs both parameter and structure learning.

See https://github.com/friguzzi/cplint/blob/master/doc/manual.pdf or http://ds.ing.unife.it/~friguzzi/software/cplint-swi/manual.html for details.

author

- Fabrizio Riguzzi, Elena Bellodi

copyright

- Fabrizio Riguzzi, Elena Bellodi

license

- Artistic License 2.0

 induce_lift(:TrainFolds:list_of_atoms, -P:probabilistic_program) is det

The predicate performs structure learning using the folds indicated in TrainFolds for training. It returns in P the learned probabilistic program.

 test_lift(:P:probabilistic_program, +TestFolds:list_of_atoms, -LL:float, -AUCROC:float, -ROC:dict, -AUCPR:float, -PR:dict) is det

The predicate takes as input in P a probabilistic program, tests P on the folds indicated in TestFolds and returns the log likelihood of the test examples in LL, the area under the Receiver Operating Characteristic curve in AUCROC, a dict containing the points of the ROC curve in ROC, the area under the Precision Recall curve in AUCPR and a dict containing the points of the PR curve in PR

 test_prob_lift(:P:probabilistic_program, +TestFolds:list_of_atoms, -NPos:int, -NNeg:int, -LL:float, -Results:list) is det

The predicate takes as input in P a probabilistic program, tests P on the folds indicated in TestFolds and returns the number of positive examples in NPos, the number of negative examples in NNeg, the log likelihood in LL and in Results a list containing the probabilistic result for each query contained in TestFolds.

 sort_rules(+RulesIn:list_of_rules, -RulesOut:list_of_rules) is det

The predicate sorts RulesIn according to the probability of the rules

 induce_par_lift(:TrainFolds:list_of_atoms, -P:probabilistic_program) is det

The predicate learns the parameters of the program stored in the in/1 fact of the input file using the folds indicated in TrainFolds for training. It returns in P the input program with the updated parameters.

 induce_par_kg(:P:probabilistic_program, -P1:probabilistic_program) is det

The predicate learns the parameters of the program stored in the in/1 fact of the input file using the folds indicated in TrainFolds for training. It returns in P the input program with the updated parameters.

 filter_rules(:RulesIn:list_of_rules, -RulesOut:list_of_rules) is det

The predicate removes the rules with a probability below or equal to the min_prob parmeter.

 filter_rules(+RulesIn:list_of_rules, -RulesOut:list_of_rules, +Min_prob:float) is det

The predicate removes from the rules with a probability below or equal to Min_prob.

 remove_zero(+RulesIn:list_of_rules, -RulesOut:list_of_rules) is det

The predicate removes the rules with a probability of 0.0.

 set_lift(:Parameter:atom, +Value:term) is det

The predicate sets the value of a parameter For a list of parameters see https://friguzzi.github.io/liftcover/

 setting_lift(:Parameter:atom, -Value:term) is det

The predicate returns the value of a parameter For a list of parameters see https://friguzzi.github.io/liftcover/

Undocumented predicates

The following predicates are exported, but not or incorrectly documented.

 prob_lift(Arg1, Arg2)

 prob_lift(Arg1, Arg2, Arg3)

 explain_lift(Arg1, Arg2)

 explain_lift(Arg1, Arg2, Arg3)

 ranked_answers(Arg1, Arg2, Arg3)

 ranked_answers(Arg1, Arg2, Arg3, Arg4)

 rank(Arg1, Arg2, Arg3)

 rank_answer(Arg1, Arg2, Arg3)

 rank_answer(Arg1, Arg2, Arg3, Arg4)

 hits_at_k(Arg1, Arg2, Arg3, Arg4, Arg5, Arg6)

 hits_at_k(Arg1, Arg2, Arg3, Arg4, Arg5, Arg6, Arg7)

 rank_ex(Arg1, Arg2, Arg3)

 rank_exs(Arg1, Arg2, Arg3, Arg4)

 inst_exs(Arg1, Arg2, Arg3, Arg4)

 induce_par_pos_kg(Arg1, Arg2)

 compute_stats_kg(Arg1, Arg2)

 compute_stats_pos_kg(Arg1, Arg2)

 compute_par_kg(Arg1, Arg2, Arg3)

 write_rules_kg(Arg1)

 write_rules_kg(Arg1, Arg2)

 write_rules_anyburl(Arg1, Arg2)

 read_rules_anyburl(Arg1, Arg2)

 rules_for_rel(Arg1, Arg2, Arg3)