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yap-6.3/packages/ProbLog/problog.yap
Vitor Santos Costa c6204240cb fix unknown nb
2010-03-03 22:14:42 +00:00

1412 lines
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Prolog

%%% -*- Mode: Prolog; -*-
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% $Date: 2009-06-17 22:22:00 +0200 (Mi, 17 Jun 2009) $
% $Revision: 1550 $
%
% This file is part of ProbLog
% http://dtai.cs.kuleuven.be/problog
%
% Copyright 2009 Katholieke Universiteit Leuven
%
% Authors: Luc De Raedt, Bernd Gutmann, Angelika Kimmig,
% Vitor Santos Costa
%
%
% Main authors of this file:
% Angelika Kimmig, Vitor Santos Costa,Bernd Gutmann
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Artistic License 2.0
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% ProbLog inference
%
% assumes probabilistic facts as Prob::Fact and clauses in normal Prolog format
%
% provides following inference modes (16/12/2008):
% - approximation with interval width Delta (IJCAI07): problog_delta(+Query,+Delta,-Low,-High,-Status)
% - bounds based on single probability threshold: problog_threshold(+Query,+Threshold,-Low,-High,-Status)
% - as above, but lower bound only: problog_low(+Query,+Threshold,-Low,-Status)
% - lower bound based on K most likely proofs: problog_kbest(+Query,+K,-Low,-Status)
% - explanation probability (ECML07): problog_max(+Query,-Prob,-FactsUsed)
% - exact probability: problog_exact(+Query,-Prob,-Status)
% - sampling: problog_montecarlo(+Query,+Delta,-Prob)
%
%
% angelika.kimmig@cs.kuleuven.be
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
:- module(problog, [problog_delta/5,
problog_threshold/5,
problog_low/4,
problog_kbest/4,
problog_kbest_save/6,
problog_max/3,
problog_exact/3,
problog_montecarlo/3,
problog_answers/2,
get_fact_probability/2,
set_fact_probability/2,
get_fact/2,
tunable_fact/2,
non_ground_fact/1,
export_facts/1,
problog_help/0,
show_inference/0,
problog_dir/1,
set_problog_flag/2,
problog_flag/2,
problog_flags/0,
op( 550, yfx, :: )]).
:- style_check(all).
:- yap_flag(unknown,error).
:- multifile user:exception/3.
user:exception(undefined_global_variable, _, fail).
% problog related modules
:- use_module('problog/flags',[set_problog_flag/2,
problog_flag/2,
problog_flags/0]).
:- use_module('problog/print', [print_sep_line/0,
print_inference/2]).
:- use_module('problog/tptree',[init_ptree/1,
delete_ptree/1,
insert_ptree/2,
count_ptree/2,
prune_check_ptree/2,
merge_ptree/3,
bdd_ptree_map/4,
bdd_ptree/3]).
% general yap modules
:- ensure_loaded(library(lists)).
:- ensure_loaded(library(terms)).
:- ensure_loaded(library(random)).
:- ensure_loaded(library(system)).
:- ensure_loaded(library(rbtrees)).
%%%%%%%%%%%%%%%%%%%%%%%%
% control predicates on various levels
%%%%%%%%%%%%%%%%%%%%%%%%
% global over all inference methods, internal use only
:- dynamic problog_predicate/2.
% global over all inference methods, exported
:- dynamic tunable_fact/2.
:- dynamic non_ground_fact/1.
:- dynamic problog_dir/1.
% global, manipulated via problog_control/2
:- dynamic up/0.
:- dynamic limit/0.
:- dynamic mc/0.
:- dynamic remember/0.
% local to problog_delta
:- dynamic low/2.
:- dynamic up/2.
:- dynamic stopDiff/1.
% local to problog_kbest
:- dynamic current_kbest/3.
% local to problog_max
:- dynamic max_probability/1.
:- dynamic max_proof/1.
% local to problog_montecarlo
:- dynamic mc_prob/1.
% local to problog_answers
:- dynamic answer/1.
% to keep track of the groundings for non-ground facts
:- dynamic grounding_is_known/2.
% for fact where the proabability is a variable
:- dynamic dynamic_probability_fact/1.
:- dynamic dynamic_probability_fact_extract/2.
% directory where ProblogBDD executable is located
% automatically set during loading -- assumes it is in same place as this file (problog.yap)
%:- getcwd(PD),retractall(problog_dir(_)),assert(problog_dir(PD)).
% yap-6 separates executables and prolog progams...
:- yap_flag(shared_object_search_path,PD),
retractall(problog_dir(_)),
assert(problog_dir(PD)).
%%%%%%%%%%%%%%%%%%%%%%%%
% help
%%%%%%%%%%%%%%%%%%%%%%%%
problog_help :-
format('~2nProbLog inference currently offers the following inference methods:~n',[]),
show_inference,
format('~2nThe following global parameters are available:~n',[]),
problog_flags,
print_sep_line,
format('~n use problog_help/0 to display this information~n',[]),
format('~n use problog_flags/0 to display current parameter values~2n',[]),
print_sep_line,
nl,
flush_output.
show_inference :-
format('~n',[]),
print_sep_line,
print_inference(call,description),
print_sep_line,
print_inference('problog_delta(+Query,+Delta,-Low,-High,-Status)','approximation with interval width Delta (IJCAI07)'),
print_inference('problog_threshold(+Query,+Threshold,-Low,-High,-Status)','bounds based on single probability threshold'),
print_inference('problog_low(+Query,+Threshold,-Low,-Status)','lower bound based on single probability threshold'),
print_inference('problog_kbest(+Query,+K,-Low,-Status)','lower bound based on K most likely proofs'),
print_inference('problog_max(+Query,-Prob,-FactsUsed)','explanation probability (ECML07)'),
print_inference('problog_exact(+Query,-Prob,-Status)','exact probability'),
print_inference('problog_montecarlo(+Query,+Delta,-Prob)','sampling with 95\%-confidence-interval-width Delta'),
print_sep_line.
%%%%%%%%%%%%%%%%%%%%%%%%
% initialization of global parameters
%%%%%%%%%%%%%%%%%%%%%%%%
init_global_params :-
set_problog_flag(bdd_time,60),
set_problog_flag(first_threshold,0.1),
L is 10**(-30),
set_problog_flag(last_threshold,L),
set_problog_flag(id_stepsize,0.5),
set_problog_flag(prunecheck,off),
set_problog_flag(maxsteps,1000),
set_problog_flag(mc_batchsize,1000),
set_problog_flag(mc_logfile,'log.txt'),
set_problog_flag(bdd_file,example_bdd),
set_problog_flag(dir,output),
set_problog_flag(save_bdd,false),
set_problog_flag(fast_proofs,false),
set_problog_flag(verbose,true).
% problog_flags,
% print_sep_line,
% format('~n use problog_help/0 for information~n',[]),
% format('~n use problog_flags/0 to display current parameter values~2n',[]),
% print_sep_line,
% nl,
% flush_output.
% parameter initialization to be called after returning to user's directory:
:- initialization(init_global_params).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% internal control flags
% if on
% - up: collect stopped derivations to build upper bound
% - limit: iterative deepening reached limit -> should go to next level
% - mc: using problog_montecarlo, i.e. proving with current sample instead of full program
% - remember: save BDD files containing script, params and mapping
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
problog_control(on,X) :-
call(X),!.
problog_control(on,X) :-
assert(X).
problog_control(off,X) :-
retractall(X).
problog_control(check,X) :-
call(X).
:- problog_control(off,up).
:- problog_control(off,mc).
:- problog_control(off,limit).
:- problog_control(off,remember).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% nice user syntax Prob::Fact
% automatic translation to internal hardware access format
%
% probabilities =1 are dropped -> normal Prolog fact
%
% internal fact representation
% - prefixes predicate name with problog_
% - adds unique ID as first argument
% - adds logarithm of probability as last argument
% - keeps original arguments in between
%
% for each predicate appearing as probabilistic fact, wrapper clause is introduced:
% - head is most general instance of original fact
% - body is corresponding version of internal fact plus call to add_to_proof/2 to update current state during proving
% example: edge(A,B) :- problog_edge(ID,A,B,LogProb), add_to_proof(ID,LogProb).
%
% dynamic predicate problog_predicate(Name,Arity) keeps track of predicates that already have wrapper clause
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
user:term_expansion(_P::( _Goal :- _Body ), _Error) :-
throw(error('we do not support this (yet?)!')).
/* this can slow down prolog time by several orders if there's lots of them
user:term_expansion(P::Goal,Goal) :-
P \= t(_),
P =:= 1,
!.
*/
user:term_expansion(P::Goal, problog:ProbFact) :-
copy_term((P,Goal),(P_Copy,Goal_Copy)),
functor(Goal, Name, Arity),
atomic_concat([problog_,Name],ProblogName),
Goal =.. [Name|Args],
append(Args,[LProb],L1),
probclause_id(ID),
ProbFact =.. [ProblogName,ID|L1],
(
(\+ var(P), P = t(TrueProb))
->
(
assert(tunable_fact(ID,TrueProb)),
LProb is log(0.5)
);
(
ground(P)
->
LProb is log(P);
(
% Probability is a variable... check wether it appears in the term
(
variable_in_term(Goal,P)
->
true;
(
format(user_error,'If you use probabilisitic facts with a variable as probabilility, the variable has to appear inside the fact.~n',[]),
format(user_error,'You used ~q in your program.~2n',[P::Goal]),
throw(non_ground_fact_error(P::Goal))
)
),
LProb=log(P),
assert(dynamic_probability_fact(ID)),
assert(dynamic_probability_fact_extract(Goal_Copy,P_Copy))
)
)
),
(
ground(Goal)
->
true;
assert(non_ground_fact(ID))
),
problog_predicate(Name, Arity, ProblogName).
% introduce wrapper clause if predicate seen first time
problog_predicate(Name, Arity, _) :-
problog_predicate(Name, Arity), !.
problog_predicate(Name, Arity, ProblogName) :-
functor(OriginalGoal, Name, Arity),
OriginalGoal =.. [_|Args],
append(Args,[Prob],L1),
ProbFact =.. [ProblogName,ID|L1],
prolog_load_context(module,Mod),
make_add_to_proof(ID2,ProbEval,AddToProof),
assert( (Mod:OriginalGoal :- ProbFact,
(
non_ground_fact(ID)
->
(non_ground_fact_grounding_id(OriginalGoal,G_ID),
atomic_concat([ID,'_',G_ID],ID2));
ID2=ID
),
% take the log of the probability (for non ground facts with variable as probability
ProbEval is Prob,
AddToProof
)),
assert( (Mod:problog_not(OriginalGoal) :- ProbFact,
(
non_ground_fact(ID)
->
( non_ground_fact_grounding_id(OriginalGoal,G_ID),
atomic_concat([ID,'_',G_ID],ID2));
ID2=ID
),
% take the log of the probability (for non ground facts with variable as probability
ProbEval is Prob,
add_to_proof_negated(ID2,ProbEval)
)),
assert(problog_predicate(Name, Arity)),
ArityPlus2 is Arity+2,
dynamic(problog:ProblogName/ArityPlus2).
make_add_to_proof(ID2,ProbEval,O) :-
problog_flag(fast_proofs,true), !,
O = fast_positive_add_to_proof(ID2,ProbEval).
make_add_to_proof(ID2,ProbEval,add_to_proof(ID2,ProbEval)).
% generate next global identifier
probclause_id(ID) :-
nb_getval(probclause_counter,ID), !,
C1 is ID+1,
nb_setval(probclause_counter,C1), !.
probclause_id(0) :-
nb_setval(probclause_counter,1).
non_ground_fact_grounding_id(Goal,ID) :-
(
ground(Goal)
->
true;
(
format(user_error,'The current program uses non-ground facts.~n', []),
format(user_error,'If you query those, you may only query fully-grounded versions of the fact.~n',[]),
format(user_error,'Within the current proof, you queried for ~q which is not ground.~n~n', [Goal]),
throw(error(non_ground_fact(Goal)))
)
),
(
grounding_is_known(Goal,ID)
->
true;
(
nb_getval(non_ground_fact_grounding_id_counter,ID),
ID2 is ID+1,
nb_setval(non_ground_fact_grounding_id_counter,ID2),
assert(grounding_is_known(Goal,ID))
)
).
reset_non_ground_facts :-
nb_setval(non_ground_fact_grounding_id_counter,0),
retractall(grounding_is_known(_,_)).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% access/update the probability of ID's fact
% hardware-access version: naively scan all problog-predicates,
% cut choice points if ID is ground (they'll all fail as ID is unique),
% but not if it isn't (used to iterate over all facts when writing out probabilities for learning)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
get_fact_probability(ID,Prob) :-
(
ground(ID) ->
get_internal_fact(ID,ProblogTerm,_ProblogName,ProblogArity),!
;
get_internal_fact(ID,ProblogTerm,_ProblogName,ProblogArity)
),
arg(ProblogArity,ProblogTerm,Log),
Prob is exp(Log).
set_fact_probability(ID,Prob) :-
get_internal_fact(ID,ProblogTerm,ProblogName,ProblogArity),
retract(ProblogTerm),
ProblogTerm =.. [ProblogName|ProblogTermArgs],
nth(ProblogArity,ProblogTermArgs,_,KeepArgs),
NewLogProb is log(Prob),
nth(ProblogArity,NewProblogTermArgs,NewLogProb,KeepArgs),
NewProblogTerm =.. [ProblogName|NewProblogTermArgs],
assert(NewProblogTerm).
get_internal_fact(ID,ProblogTerm,ProblogName,ProblogArity) :-
problog_predicate(Name,Arity),
atomic_concat([problog_,Name],ProblogName),
ProblogArity is Arity+2,
functor(ProblogTerm,ProblogName,ProblogArity),
arg(1,ProblogTerm,ID),
call(ProblogTerm).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% writing those facts with learnable parameters to File
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
export_facts(File) :-
tell(File),
export_facts,
flush_output,
told.
export_facts :-
tunable_fact(ID,_),
once(write_tunable_fact(ID)),
fail.
export_facts.
write_tunable_fact(ID) :-
get_internal_fact(ID,ProblogTerm,ProblogName,ProblogArity),
ProblogTerm =.. [_Functor,ID|Args],
atomic_concat('problog_',OutsideFunctor,ProblogName),
Last is ProblogArity-1,
nth(Last,Args,LogProb,OutsideArgs),
OutsideTerm =.. [OutsideFunctor|OutsideArgs],
Prob is exp(LogProb),
format('~w :: ~q.~n',[Prob,OutsideTerm]).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% recover fact for given id
% list version not exported (yet?)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% ID of ground fact
get_fact(ID,OutsideTerm) :-
get_internal_fact(ID,ProblogTerm,ProblogName,ProblogArity),
!,
ProblogTerm =.. [_Functor,ID|Args],
atomic_concat('problog_',OutsideFunctor,ProblogName),
Last is ProblogArity-1,
nth(Last,Args,_LogProb,OutsideArgs),
OutsideTerm =.. [OutsideFunctor|OutsideArgs].
% ID of instance of non-ground fact: get fact from grounding table
get_fact(ID,OutsideTerm) :-
recover_grounding_id(ID,GID),
grounding_is_known(OutsideTerm,GID).
recover_grounding_id(Atom,ID) :-
name(Atom,List),
reverse(List,Rev),
recover_number(Rev,NumRev),
reverse(NumRev,Num),
name(ID,Num).
recover_number([95|_],[]) :- !. % name('_',[95])
recover_number([A|B],[A|C]) :-
recover_number(B,C).
get_fact_list([],[]).
get_fact_list([ID|IDs],[Fact|Facts]) :-
(ID=not(X) -> Fact=not(Y); Fact=Y, ID=X),
get_fact(X,Y),
get_fact_list(IDs,Facts).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% ProbLog inference, core methods
%
% state of proving saved in two backtrackable global variables
% - problog_current_proof holds list of IDs of clauses used
% - problog_probability holds the sum of their log probabilities
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% called "inside" probabilistic facts to update current state of proving
% if number of steps exceeded, fail
% if fact used before, succeed and keep status as is
% if not prunable, calculate probability and
% if threshold exceeded, add stopped derivation to upper bound and fail
% else update state and succeed
%
% do not maintain gloabl variables in montecarlo mode
add_to_proof(ID,Prob) :-
(
problog_control(check,mc)
->
montecarlo_check(ID)
;
b_getval(problog_steps,MaxSteps),
b_getval(problog_probability, CurrentP),
nb_getval(problog_threshold, CurrentThreshold),
b_getval(problog_current_proof, IDs),
%%%% Bernd, changes for negated ground facts
\+ memberchk(not(ID),IDs),
%%%% Bernd, changes for negated ground facts
(
MaxSteps =< 0
->
fail
;
(
memberchk(ID, IDs)
->
true
;
\+ prune_check([ID|IDs],1),
multiply_probabilities(CurrentP, Prob, NProb),
(
NProb < CurrentThreshold
->
upper_bound([ID|IDs]),
fail
;
b_setval(problog_probability, NProb),
b_setval(problog_current_proof, [ID|IDs])
)
),
Steps is MaxSteps-1,
b_setval(problog_steps,Steps)
)
).
% this is a version for long proofs, it assumes we are using
% tries, it assumes all our proofs never reuse ProbLog facts, and it assumes
% ground positive literals only.
fast_positive_add_to_proof(ID,Prob) :-
b_getval(problog_probability, CurrentP),
nb_getval(problog_threshold, CurrentThreshold),
multiply_probabilities(CurrentP, Prob, NProb),
b_getval(problog_current_proof, IDs),
(
NProb < CurrentThreshold
->
upper_bound([ID|IDs]),
fail
;
b_setval(problog_probability, NProb),
b_setval(problog_current_proof, [ID|IDs])
).
%%%% Bernd, changes for negated ground facts
add_to_proof_negated(ID,Prob) :-
(
problog_control(check,mc)
->
% the sample has to fail if the fact is negated
\+ montecarlo_check(ID)
;
b_getval(problog_steps,MaxSteps),
b_getval(problog_probability, CurrentP),
nb_getval(problog_threshold, CurrentThreshold),
b_getval(problog_current_proof, IDs),
\+ memberchk(ID,IDs),
(
MaxSteps =< 0
->
fail
;
(
memberchk(not(ID), IDs)
->
true
;
% \+ prune_check([ID|IDs],1),
InverseProb is log(1 - exp(Prob)),
multiply_probabilities(CurrentP, InverseProb, NProb),
(
NProb < CurrentThreshold
->
upper_bound([not(ID)|IDs]), %% checkme
fail
;
b_setval(problog_probability, NProb),
b_setval(problog_current_proof, [not(ID)|IDs])
)
),
Steps is MaxSteps-1,
b_setval(problog_steps,Steps)
)
).
%%%% Bernd, changes for negated ground facts
% if in monte carlo mode, check array to see if fact can be used
montecarlo_check(ID) :-
array_element(mc_sample,ID,V),
(
V == 1 -> true
;
V == 2 -> fail
;
new_sample(ID)
).
new_sample(ID) :-
get_fact_probability(ID,Prob),
random(R),
R<Prob,
!,
update_array(mc_sample,ID,1).
new_sample(ID) :-
update_array(mc_sample,ID,2),
fail.
% if threshold reached, remember this by setting limit to on, then
% if up is on, store stopped derivation in second trie
%
% List always length>=1 -> don't need []=true-case for tries
upper_bound(List) :-
problog_control(on,limit),
problog_control(check,up),
reverse(List,R),
(prune_check(R,2) -> true; insert_ptree(R,2)).
multiply_probabilities(CurrentLogP, LogProb, NLogProb) :-
NLogProb is CurrentLogP+LogProb.
% this is called by all inference methods before the actual ProbLog goal
% to set up environment for proving
init_problog(Threshold) :-
reset_non_ground_facts,
LT is log(Threshold),
b_setval(problog_probability, 0.0),
b_setval(problog_current_proof, []),
nb_setval(problog_threshold, LT),
problog_flag(maxsteps,MaxS),
b_setval(problog_steps, MaxS),
problog_control(off,limit).
% idea: proofs that are refinements of known proof can be pruned as they don't add probability mass
% note that current ptree implementation doesn't provide the check as there's no efficient method known so far...
prune_check(Proof,TreeID) :-
problog_flag(prunecheck,on),
prune_check_ptree(Proof,TreeID).
% to call a ProbLog goal, patch all subgoals with the user's module context
% (as logical part is there, but probabilistic part in problog)
problog_call(Goal) :-
yap_flag(typein_module,Module),
%%% if user provides init_db, call this before proving goal
(current_predicate(_,Module:init_db) -> call(Module:init_db); true),
put_module(Goal,Module,ModGoal),
call(ModGoal).
put_module((Mod:Goal,Rest),Module,(Mod:Goal,Transformed)) :-
!,
put_module(Rest,Module,Transformed).
put_module((Goal,Rest),Module,(Module:Goal,Transformed)) :-
!,
put_module(Rest,Module,Transformed).
put_module((Mod:Goal),_Module,(Mod:Goal)) :-
!.
put_module(Goal,Module,Module:Goal).
% end of core
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% evaluating a DNF given as trie using BDD
% input: ID of trie to be used
% output: probability and status (to catch potential failures/timeouts from outside)
%
% with internal BDD timeout (set using problog flag bdd_time)
%
% bdd_ptree/3 constructs files for ProblogBDD from the trie
%
% if calling ProblogBDD doesn't exit successfully, status will be timeout
%
% writes number of proofs in trie and BDD time to standard user output
%
% if remember is on, input files for ProblogBDD will be saved
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
eval_dnf(ID,Prob,Status) :-
((ID = 1, problog_flag(save_bdd,true)) -> problog_control(on,remember); problog_control(off,remember)),
count_ptree(ID,NX),
(
problog_flag(verbose,true)
->
(
NX=1
->
format(user,'1 proof~n',[]);
format(user,'~w proofs~n',[NX])
);
true
),
problog_flag(dir,DirFlag),
problog_flag(bdd_file,BDDFileFlag),
atomic_concat([DirFlag,BDDFileFlag],BDDFile),
problog_flag(bdd_par_file,BDDParFileFlag),
atomic_concat([DirFlag,BDDParFileFlag],BDDParFile),
(problog_control(check,remember) ->
bdd_ptree_map(ID,BDDFile,BDDParFile,Mapping),
atomic_concat([DirFlag,'save_map'],MapFile),
tell(MapFile),
format('mapping(~q).~n',[Mapping]),
flush_output,
told
;
bdd_ptree(ID,BDDFile,BDDParFile)
),
problog_flag(bdd_time,BDDTime),
problog_flag(bdd_result,ResultFileFlag),
atomic_concat([DirFlag,ResultFileFlag],ResultFile),
problog_dir(PD),
atomic_concat([PD,'/ProblogBDD -l ',BDDFile,' -i ',BDDParFile,' -m p -t ', BDDTime,' > ', ResultFile],Command),
statistics(walltime,_),
shell(Command,Return),
(
Return =\= 0
->
Status = timeout
;
(
statistics(walltime,[_,E3]),
(problog_flag(verbose,true) -> format(user,'~w ms BDD processing~n',[E3]);true),
see(ResultFile),
read(probability(Prob)),
seen,
delete_file(ResultFile),
Status = ok
)
),
(problog_control(check,remember) ->
atomic_concat([DirFlag,'save_script'],SaveBDDFile),
rename_file(BDDFile,SaveBDDFile),
atomic_concat([DirFlag,'save_params'],SaveBDDParFile),
rename_file(BDDParFile,SaveBDDParFile)
;
true
),
problog_control(off,remember).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% different inference methods
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% approximate inference: bounds based on single probability threshold
% problog_threshold(+Goal,+Threshold,-LowerBound,-UpperBound,-Status)
%
% use backtracking over problog_call to get all solutions
%
% trie 1 collects proofs, trie 2 collects stopped derivations, trie 3 is used to unit them for the upper bound
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
problog_threshold(Goal, Threshold, _, _, _) :-
problog_control(on,up),
init_problog_threshold(Threshold),
problog_call(Goal),
add_solution,
fail.
problog_threshold(_, _, LP, UP, Status) :-
compute_bounds(LP, UP, Status).
init_problog_threshold(Threshold) :-
init_ptree(1),
init_ptree(2),
init_problog(Threshold).
add_solution :-
b_getval(problog_current_proof, IDs),
(IDs == [] -> R = true ; reverse(IDs,R)),
insert_ptree(R,1).
compute_bounds(LP, UP, Status) :-
eval_dnf(1,LP,StatusLow),
(StatusLow \== ok ->
Status = StatusLow
;
merge_ptree(1,2,3),
eval_dnf(3,UP,Status)),
delete_ptree(1),
delete_ptree(2),
delete_ptree(3).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% approximate inference: lower bound based on all proofs above probability threshold
% problog_low(+Goal,+Threshold,-LowerBound,-Status)
%
% same as problog_threshold/5, but lower bound only (no stopped derivations stored)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
problog_low(Goal, Threshold, _, _) :-
problog_control(off,up),
init_problog_low(Threshold),
problog_call(Goal),
add_solution,
fail.
problog_low(_, _, LP, Status) :-
eval_dnf(1,LP,Status),
delete_ptree(1).
init_problog_low(Threshold) :-
init_ptree(1),
init_problog(Threshold).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% approximate inference: bounds by iterative deepening up to interval width Delta
% problog_delta(+Goal,+Delta,-LowerBound,-UpperBound,-Status)
%
% wraps iterative deepening around problog_threshold, i.e.
% - starts with threshold given by first_threshold flag
% - if Up-Low >= Delta, multiply threshold by factor given in id_stepsize flag and iterate
% (does not use problog_threshold as trie 1 is kept over entire search)
%
% local dynamic predicates low/2, up/2, stopDiff/1
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
problog_delta(Goal, Delta, Low, Up, Status) :-
problog_control(on,up),
problog_flag(first_threshold,InitT),
init_problog_delta(InitT,Delta),
problog_delta_id(Goal,Status),
delete_ptree(1),
delete_ptree(2),
(retract(low(_,Low)) -> true; true),
(retract(up(_,Up)) -> true; true).
init_problog_delta(Threshold,Delta) :-
retractall(low(_,_)),
retractall(up(_,_)),
retractall(stopDiff(_)),
init_ptree(1),
init_ptree(2),
assert(low(0,0.0)),
assert(up(0,1.0)),
assert(stopDiff(Delta)),
init_problog(Threshold).
problog_delta_id(Goal, _) :-
problog_call(Goal),
add_solution, % reused from problog_threshold
fail.
problog_delta_id(Goal, Status) :-
evaluateStep(Ans,StatusE),
problog_flag(last_threshold_log,Stop),
nb_getval(problog_threshold,Min),
(StatusE \== ok ->
Status = StatusE
;
(
Ans = 1 ->
Status = ok
;
Min =< Stop ->
Status = stopreached
;
problog_control(check,limit) ->
problog_control(off,limit),
problog_flag(id_stepsize_log,Step),
New is Min+Step,
nb_setval(problog_threshold,New),
problog_delta_id(Goal, Status)
;
true
)).
% call the dnf evaluation where needed
evaluateStep(Ans,Status) :- once(evalStep(Ans,Status)).
evalStep(Ans,Status) :-
stopDiff(Delta),
count_ptree(1,NProofs),
count_ptree(2,NCands),
(problog_flag(verbose,true) -> format(user,'~w proofs, ~w stopped derivations~n',[NProofs,NCands]);true),
flush_output(user),
eval_lower(NProofs,Low,StatusLow),
(StatusLow \== ok ->
Status = StatusLow
;
up(_,OUP),
IntDiff is OUP-Low,
((IntDiff < Delta; IntDiff =:= 0) ->
Up=OUP, StatusUp = ok
;
eval_upper(NCands,Up,StatusUp),
delete_ptree(2),
init_ptree(2),
delete_ptree(3)
),
(StatusUp \== ok ->
Status = StatusUp
;
Diff is Up-Low,
(problog_flag(verbose,true) -> format(user,'difference: ~6f~n',[Diff]);true),
flush_output(user),
((Diff < Delta; Diff =:= 0) -> Ans = 1; Ans = 0),
Status = ok)).
% no need to re-evaluate if no new proofs found on this level
eval_lower(N,P,ok) :-
low(N,P).
% evaluate if there are proofs
eval_lower(N,P,Status) :-
N > 0,
low(OldN,_),
N \= OldN,
eval_dnf(1,P,Status),
(Status = ok ->
retract(low(_,_)),
assert(low(N,P)),
(problog_flag(verbose,true) -> format(user,'lower bound: ~6f~n',[P]);true),
flush_output(user)
;
true).
% if no stopped derivations, up=low
eval_upper(0,P,ok) :-
retractall(up(_,_)),
low(N,P),
assert(up(N,P)).
% else merge proofs and stopped derivations to get upper bound
% in case of timeout or other problems, skip and use bound from last level
eval_upper(N,UpP,ok) :-
N > 0,
merge_ptree(1,2,3),
eval_dnf(3,UpP,StatusUp),
(StatusUp = ok ->
retract(up(_,_)),
assert(up(N,UpP))
;
(problog_flag(verbose,true) -> format(user,'~w - continue using old up~n',[StatusUp]);true),
flush_output(user),
up(_,UpP)).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% explanation probability - returns list of facts used or constant 'unprovable' as third argument
% problog_max(+Goal,-Prob,-Facts)
%
% uses iterative deepening with samw parameters as bounding algorithm
% threshold gets adapted whenever better proof is found
%
% uses local dynamic predicates max_probability/1 and max_proof/1
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
problog_max(Goal, Prob, Facts) :-
problog_control(off,up),
problog_flag(first_threshold,InitT),
init_problog_max(InitT),
problog_max_id(Goal, Prob, FactIDs),
( FactIDs = [_|_] -> get_fact_list(FactIDs,Facts);
Facts = FactIDs).
init_problog_max(Threshold) :-
retractall(max_probability(_)),
retractall(max_proof(_)),
assert(max_probability(-999999)),
assert(max_proof(unprovable)),
init_problog(Threshold).
update_max :-
b_getval(problog_probability,CurrP),
max_probability(MaxP),
(CurrP =< MaxP ->
fail
;
b_getval(problog_current_proof, IDs),
reverse(IDs,R),
retractall(max_proof(_)),
assert(max_proof(R)),
nb_setval(problog_threshold, CurrP),
retractall(max_probability(_)),
assert(max_probability(CurrP))).
problog_max_id(Goal, _Prob, _Clauses) :-
problog_call(Goal),
update_max,
fail.
problog_max_id(Goal, Prob, Clauses) :-
max_probability(MaxP),
nb_getval(problog_threshold, LT),
problog_flag(last_threshold_log,ToSmall),
((MaxP >= LT ; \+ problog_control(check,limit); LT < ToSmall) ->
((max_proof(unprovable), problog_control(check,limit), LT < ToSmall) ->
problog_flag(last_threshold,Stopping),
Clauses = unprovable(Stopping)
; max_proof(Clauses)),
Prob is exp(MaxP)
;
problog_flag(id_stepsize_log,Step),
NewLT is LT+Step,
nb_setval(problog_threshold, NewLT),
problog_control(off,limit),
problog_max_id(Goal, Prob, Clauses)).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% lower bound using k best proofs
% problog_kbest(+Goal,+K,-Prob,-Status)
%
% does iterative deepening search similar to problog_max, but for k(>=1) most likely proofs
% afterwards uses BDD evaluation to calculate probability (also for k=1 -> uniform treatment in learning)
%
% uses dynamic local predicate current_kbest/3 to collect proofs,
% only builds trie at the end (as probabilities of single proofs are important here)
%
% note: >k proofs will be used if the one at position k shares its probability with others,
% as all proofs with that probability will be included
%
% version with _save at the end renames files for ProblogBDD to keep them
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
problog_kbest_save(Goal, K, Prob, Status, BDDFile, ParamFile) :-
problog_kbest(Goal, K, Prob, Status),
( Status=ok ->
problog_flag(bdd_file,InternBDDFlag),
problog_flag(bdd_par_file,InternParFlag),
problog_flag(dir,DirFlag),
atomic_concat([DirFlag,InternBDDFlag],InternBDD),
atomic_concat([DirFlag,InternParFlag],InternPar),
rename_file(InternBDD,BDDFile),
rename_file(InternPar,ParamFile)
;
true).
problog_kbest(Goal, K, Prob, Status) :-
problog_control(off,up),
problog_flag(first_threshold,InitT),
init_problog_kbest(InitT),
problog_kbest_id(Goal, K),
retract(current_kbest(_,ListFound,_NumFound)),
build_prefixtree(ListFound),
eval_dnf(1,Prob,Status),
delete_ptree(1).
init_problog_kbest(Threshold) :-
retractall(current_kbest(_,_,_)),
assert(current_kbest(-999999,[],0)), %(log-threshold,proofs,num_proofs)
init_ptree(1),
init_problog(Threshold).
problog_kbest_id(Goal, K) :-
problog_call(Goal),
update_kbest(K),
fail.
problog_kbest_id(Goal, K) :-
current_kbest(CurrentBorder,_,Found),
nb_getval(problog_threshold, Min),
problog_flag(last_threshold_log,ToSmall),
((Found>=K ; \+ problog_control(check,limit) ; Min < CurrentBorder ; Min < ToSmall) ->
true
;
problog_flag(id_stepsize_log,Step),
NewLT is Min+Step,
nb_setval(problog_threshold, NewLT),
problog_control(off,limit),
problog_kbest_id(Goal, K)).
update_kbest(K) :-
b_getval(problog_probability,NewLogProb),
current_kbest(LogThreshold,_,_),
(NewLogProb>=LogThreshold ->
b_getval(problog_current_proof,RevProof),
reverse(RevProof,Proof),
update_current_kbest(K,NewLogProb,Proof)
;
fail).
update_current_kbest(_,NewLogProb,Cl) :-
current_kbest(_,List,_),
memberchk(NewLogProb-Cl,List),
!.
update_current_kbest(K,NewLogProb,Cl) :-
retract(current_kbest(OldThres,List,Length)),
sorted_insert(NewLogProb-Cl,List,NewList),
NewLength is Length+1,
(NewLength < K ->
assert(current_kbest(OldThres,NewList,NewLength))
;
(NewLength>K ->
First is NewLength-K+1,
cutoff(NewList,NewLength,First,FinalList,FinalLength)
; FinalList=NewList, FinalLength=NewLength),
FinalList=[NewThres-_|_],
nb_setval(problog_threshold,NewThres),
assert(current_kbest(NewThres,FinalList,FinalLength))).
sorted_insert(A,[],[A]).
sorted_insert(A-LA,[B1-LB1|B], [A-LA,B1-LB1|B] ) :-
A =< B1.
sorted_insert(A-LA,[B1-LB1|B], [B1-LB1|C] ) :-
A > B1,
sorted_insert(A-LA,B,C).
% keeps all entries with lowest probability, even if implying a total of more than k
cutoff(List,Len,1,List,Len) :- !.
cutoff([P-L|List],Length,First,[P-L|List],Length) :-
nth(First,[P-L|List],PF-_),
PF=:=P,
!.
cutoff([_|List],Length,First,NewList,NewLength) :-
NextFirst is First-1,
NextLength is Length-1,
cutoff(List,NextLength,NextFirst,NewList,NewLength).
build_prefixtree([]).
build_prefixtree([_-[]|_List]) :-
!,
insert_ptree(true,1).
build_prefixtree([_-L|List]) :-
insert_ptree(L,1),
build_prefixtree(List).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% exact probability
% problog_exact(+Goal,-Prob,-Status)
%
% using all proofs = using all proofs with probability > 0
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
problog_exact(Goal,Prob,Status) :-
problog_low(Goal,0,Prob,Status).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% probability by sampling:
% running another N samples until 95percentCI-width<Delta
% lazy sampling using three-valued array indexed by internal fact IDs
%
% still collects actual proofs found in samples in ptree, though this is no longer used
% by method itself, only to write number to log-file
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
problog_montecarlo(_,_,_) :-
non_ground_fact(_),
!,
format(user_error,'Current database contains non-ground facts.',[]),
format(user_error,'Monte Carlo inference is not possible in this case. Try k-best instead.',[]),
fail.
problog_montecarlo(Goal,Delta,Prob) :-
retractall(mc_prob(_)),
nb_getval(probclause_counter,ID), !,
C is ID+1,
static_array(mc_sample,C,char),
problog_control(off,up),
problog_flag(mc_batchsize,N),
problog_flag(mc_logfile,File1),
problog_flag(dir,Dir),
atomic_concat([Dir,File1],File),
montecarlo(Goal,Delta,N,File),
retract(mc_prob(Prob)).
montecarlo(Goal,Delta,K,File) :-
reset_static_array(mc_sample),
problog_control(on,mc),
open(File,write,Log),
format(Log,'# goal: ~q~n#delta: ~w~n',[Goal,Delta]),
format(Log,'# num_programs prob low high diff time~2n',[]),
close(Log),
statistics(walltime,[T1,_]),
(problog_flag(verbose,true) -> format('search for ~q~n',[Goal]);true),
montecarlo(Goal,Delta,K,0,File,0,T1),
problog_control(off,mc).
% calculate values after K samples
montecarlo(Goal,Delta,K,SamplesSoFar,File,PositiveSoFar,InitialTime) :-
SamplesNew is SamplesSoFar+1,
SamplesNew mod K =:= 0,
!,
copy_term(Goal,GoalC),
(mc_prove(GoalC) -> Next is PositiveSoFar+1; Next=PositiveSoFar),
Prob is Next/SamplesNew,
Epsilon is 2*sqrt(Prob*(1-Prob)/SamplesNew),
Low is Prob-Epsilon,
High is Prob+Epsilon,
Diff is 2*Epsilon,
statistics(walltime,[T2,_]),
Time is (T2-InitialTime)/1000,
(problog_flag(verbose,true) -> format('~n~w samples~nestimated probability ~w~n95 percent confidence interval [~w,~w]~n',[SamplesNew,Prob,Low,High]);true),
open(File,append,Log),
format(Log,'~w ~8f ~8f ~8f ~8f ~3f~n',[SamplesNew,Prob,Low,High,Diff,Time]),
close(Log),
((Diff<Delta; Diff =:= 0) -> (problog_flag(verbose,true) -> format('Runtime ~w sec~2n',[Time]);true),assert(mc_prob(Prob))
;
montecarlo(Goal,Delta,K,SamplesNew,File,Next,InitialTime)).
% continue until next K samples done
montecarlo(Goal,Delta,K,SamplesSoFar,File,PositiveSoFar,InitialTime) :-
SamplesNew is SamplesSoFar+1,
copy_term(Goal,GoalC),
(mc_prove(GoalC) -> Next is PositiveSoFar+1; Next=PositiveSoFar),
montecarlo(Goal,Delta,K,SamplesNew,File,Next,InitialTime).
mc_prove(A) :- !,
(get_some_proof(A) ->
clean_sample
;
clean_sample,fail
).
clean_sample :-
reset_static_array(mc_sample),
fail.
clean_sample.
% find new proof
get_some_proof(Goal) :-
init_problog(0),
problog_call(Goal).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% exact probability of all ground instances of Goal
% output goes to File
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
problog_answers(Goal,File) :-
set_problog_flag(verbose,false),
retractall(answer(_)),
% this will not give the exact prob of Goal!
problog_exact((Goal,ground(Goal),\+problog:answer(Goal),assert(problog:answer(Goal))),_,_),
open(File,write,_,[alias(answer)]),
eval_answers,
close(answer).
eval_answers :-
retract(answer(G)),
problog_exact(G,P,_),
format(answer,'answer(~q,~w).~n',[G,P]),
fail.
eval_answers.