liftcover.pl

 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.

 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/

 explain_lift(:At:atom, -Exp:list) is multi

The predicate returns the explanation of atom At given by the input program. The first argument of At should be the model name. The explanation is a list of pairs (P-Ex) where P is the probability in the head of a rule H:P:-B and Ex is a true grounding of B.

 explain_lift(:At:atom, +Program:probabilistic_program, -Exp:list) is multi

The predicate returns the explanation of atom At given by Program.

 hits_at_k(:Folds:list_of_atoms, +TargetPred:predicate, +Arg:int, +K:int, -HitsAtK:float, -FilteredHitsAtK:float) is det

Returns the Hits@K and filtered Hits@K of the target predicate TargetPred on the list of folds Folds for the argument in position Arg.

 hits_at_k(:Folds:list_of_atoms, +TargetPred:predicate, +Arg:int, +Prog:probabilistic_program, +K:int, -Hits:float, -FilteredHits:float) is det

Returns the Hits@K and filtered Hits@K of the target predicate TargetPred on the list of folds Folds for the argument in position Arg computed over Prog.

 inst_exs(:Folds:list, +TargetPred:PredSpec, +Arg:int, +ProbabilisticProgram:list_of_probabilistic_clauses) is det

The predicate prints the list of answers for all the triples in Folds for predicate TaragetPredwhere argument in position Arg has been replaced by a variable.

 rank_exs(:Folds:list, +TargetPred:PredSpec, +Arg:int, +ProbabilisticProgram:list_of_probabilistic_clauses) is det

The predicate prints the list of answers for all the triples in Folds for predicate TaragetPredwhere argument in position Arg has been replaced by a variable.

 rank_ex(:At:atom, +ProbabilisticProgram:list_of_probabilistic_clauses, +Arg:int) is det

The predicate prints the list of answers for the query At where argument in position Arg has been replaced by a variable. The first argument of At should be the model name.

 rank_answer(:At:atom, +Arg:integer, -Rank:float) is det

The predicate returns the rank of the constant in argument Arg of At in the list of answers for the query At.

 rank_answer(:At:atom, +Arg:integer, +Prog:probabilistic_program, -Rank:float) is det

The predicate returns the rank of the constant in argument Arg of At in the list of answers for the query At asked using the program Prog.

 ranked_answers(:At:atom, +Var:var, -RankedAnswers:list) is multi

The predicate returns a list of answers for the query At. Var should be a variable in At. RankedAnswers is a list of pairs (P-A) where P is the probability of the answer At{Var/A}. The list is sorted in decreasing order of probability. The first argument of At should be the model name. The query is asked to the input program.

 ranked_answers(:At:atom, +Var:var, +Prog:probabilistic_program, -RankedAnswers:list) is multi

As ranked_answers/3 but the query is asked to the program Prog.

 rank(:Element:term, +OrderedList:list, -Rank:float) is det

The predicate returns the rank of Element in the list OrderedList. Group of records with the same value are assigned the average of the ranks. OrderedList is a list of pairs (S - E) where S is the score and E is the element.

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rank.html

 prob_lift(:At:atom, -P:float) is multi

The predicate computes the probability of atom At given by the input program. The first argument of At should be the model name. If At contains variables, the predicate returns all the instantiaions of At with their probabilities in backtracking.

 prob_lift(:At:atom, +Program:probabilistic_program, -P:float) is multi

The predicate computes the probability of atom At given by Program. The first argument of At should be the model name. If At contains variables, the predicate returns all the instantiaions of At with their probabilities in backtracking.