%%% -*- Mode: Prolog; -*- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % $Date: 2010-10-06 12:56:13 +0200 (Wed, 06 Oct 2010) $ % $Revision: 4877 $ % % This file is part of ProbLog % http://dtai.cs.kuleuven.be/problog % % ProbLog was developed at Katholieke Universiteit Leuven % % Copyright 2008, 2009, 2010 % Katholieke Universiteit Leuven % % Main authors of this file: % Angelika Kimmig, Vitor Santos Costa,Bernd Gutmann, % Theofrastos Mantadelis, Guy Van den Broeck % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % Artistic License 2.0 % % Copyright (c) 2000-2006, The Perl Foundation. % % Everyone is permitted to copy and distribute verbatim copies of this % license document, but changing it is not allowed. Preamble % % This license establishes the terms under which a given free software % Package may be copied, modified, distributed, and/or % redistributed. The intent is that the Copyright Holder maintains some % artistic control over the development of that Package while still % keeping the Package available as open source and free software. % % You are always permitted to make arrangements wholly outside of this % license directly with the Copyright Holder of a given Package. If the % terms of this license do not permit the full use that you propose to % make of the Package, you should contact the Copyright Holder and seek % a different licensing arrangement. Definitions % % "Copyright Holder" means the individual(s) or organization(s) named in % the copyright notice for the entire Package. % % "Contributor" means any party that has contributed code or other % material to the Package, in accordance with the Copyright Holder's % procedures. % % "You" and "your" means any person who would like to copy, distribute, % or modify the Package. % % "Package" means the collection of files distributed by the Copyright % Holder, and derivatives of that collection and/or of those files. A % given Package may consist of either the Standard Version, or a % Modified Version. % % "Distribute" means providing a copy of the Package or making it % accessible to anyone else, or in the case of a company or % organization, to others outside of your company or organization. % % "Distributor Fee" means any fee that you charge for Distributing this % Package or providing support for this Package to another party. It % does not mean licensing fees. % % "Standard Version" refers to the Package if it has not been modified, % or has been modified only in ways explicitly requested by the % Copyright Holder. % % "Modified Version" means the Package, if it has been changed, and such % changes were not explicitly requested by the Copyright Holder. % % "Original License" means this Artistic License as Distributed with the % Standard Version of the Package, in its current version or as it may % be modified by The Perl Foundation in the future. % % "Source" form means the source code, documentation source, and % configuration files for the Package. % % "Compiled" form means the compiled bytecode, object code, binary, or % any other form resulting from mechanical transformation or translation % of the Source form. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % Permission for Use and Modification Without Distribution % % (1) You are permitted to use the Standard Version and create and use % Modified Versions for any purpose without restriction, provided that % you do not Distribute the Modified Version. % % Permissions for Redistribution of the Standard Version % % (2) You may Distribute verbatim copies of the Source form of the % Standard Version of this Package in any medium without restriction, % either gratis or for a Distributor Fee, provided that you duplicate % all of the original copyright notices and associated disclaimers. At % your discretion, such verbatim copies may or may not include a % Compiled form of the Package. % % (3) You may apply any bug fixes, portability changes, and other % modifications made available from the Copyright Holder. The resulting % Package will still be considered the Standard Version, and as such % will be subject to the Original License. % % Distribution of Modified Versions of the Package as Source % % (4) You may Distribute your Modified Version as Source (either gratis % or for a Distributor Fee, and with or without a Compiled form of the % Modified Version) provided that you clearly document how it differs % from the Standard Version, including, but not limited to, documenting % any non-standard features, executables, or modules, and provided that % you do at least ONE of the following: % % (a) make the Modified Version available to the Copyright Holder of the % Standard Version, under the Original License, so that the Copyright % Holder may include your modifications in the Standard Version. (b) % ensure that installation of your Modified Version does not prevent the % user installing or running the Standard Version. In addition, the % modified Version must bear a name that is different from the name of % the Standard Version. (c) allow anyone who receives a copy of the % Modified Version to make the Source form of the Modified Version % available to others under (i) the Original License or (ii) a license % that permits the licensee to freely copy, modify and redistribute the % Modified Version using the same licensing terms that apply to the copy % that the licensee received, and requires that the Source form of the % Modified Version, and of any works derived from it, be made freely % available in that license fees are prohibited but Distributor Fees are % allowed. % % Distribution of Compiled Forms of the Standard Version or % Modified Versions without the Source % % (5) You may Distribute Compiled forms of the Standard Version without % the Source, provided that you include complete instructions on how to % get the Source of the Standard Version. Such instructions must be % valid at the time of your distribution. If these instructions, at any % time while you are carrying out such distribution, become invalid, you % must provide new instructions on demand or cease further % distribution. If you provide valid instructions or cease distribution % within thirty days after you become aware that the instructions are % invalid, then you do not forfeit any of your rights under this % license. % % (6) You may Distribute a Modified Version in Compiled form without the % Source, provided that you comply with Section 4 with respect to the % Source of the Modified Version. % % Aggregating or Linking the Package % % (7) You may aggregate the Package (either the Standard Version or % Modified Version) with other packages and Distribute the resulting % aggregation provided that you do not charge a licensing fee for the % Package. Distributor Fees are permitted, and licensing fees for other % components in the aggregation are permitted. The terms of this license % apply to the use and Distribution of the Standard or Modified Versions % as included in the aggregation. % % (8) You are permitted to link Modified and Standard Versions with % other works, to embed the Package in a larger work of your own, or to % build stand-alone binary or bytecode versions of applications that % include the Package, and Distribute the result without restriction, % provided the result does not expose a direct interface to the Package. % % Items That are Not Considered Part of a Modified Version % % (9) Works (including, but not limited to, modules and scripts) that % merely extend or make use of the Package, do not, by themselves, cause % the Package to be a Modified Version. In addition, such works are not % considered parts of the Package itself, and are not subject to the % terms of this license. % % General Provisions % % (10) Any use, modification, and distribution of the Standard or % Modified Versions is governed by this Artistic License. By using, % modifying or distributing the Package, you accept this license. Do not % use, modify, or distribute the Package, if you do not accept this % license. % % (11) If your Modified Version has been derived from a Modified Version % made by someone other than you, you are nevertheless required to % ensure that your Modified Version complies with the requirements of % this license. % % (12) This license does not grant you the right to use any trademark, % service mark, tradename, or logo of the Copyright Holder. % % (13) This license includes the non-exclusive, worldwide, % free-of-charge patent license to make, have made, use, offer to sell, % sell, import and otherwise transfer the Package with respect to any % patent claims licensable by the Copyright Holder that are necessarily % infringed by the Package. If you institute patent litigation % (including a cross-claim or counterclaim) against any party alleging % that the Package constitutes direct or contributory patent % infringement, then this Artistic License to you shall terminate on the % date that such litigation is filed. % % (14) Disclaimer of Warranty: THE PACKAGE IS PROVIDED BY THE COPYRIGHT % HOLDER AND CONTRIBUTORS "AS IS' AND WITHOUT ANY EXPRESS OR IMPLIED % WARRANTIES. THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A % PARTICULAR PURPOSE, OR NON-INFRINGEMENT ARE DISCLAIMED TO THE EXTENT % PERMITTED BY YOUR LOCAL LAW. UNLESS REQUIRED BY LAW, NO COPYRIGHT % HOLDER OR CONTRIBUTOR WILL BE LIABLE FOR ANY DIRECT, INDIRECT, % INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING IN ANY WAY OUT OF THE USE % OF THE PACKAGE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 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_exact_save/5, problog_montecarlo/3, problog_dnf_sampling/3, problog_answers/2, problog_kbest_answers/3, problog_table/1, clear_retained_tables/0, problog_neg/1, get_fact_probability/2, set_fact_probability/2, get_continuous_fact_parameters/2, set_continuous_fact_parameters/2, get_fact/2, tunable_fact/2, tunable_continuous_fact/2, continuous_fact/1, non_ground_fact/1, export_facts/1, problog_help/0, problog_version/0, show_inference/0, problog_dir/1, set_problog_flag/2, problog_flag/2, problog_flags/0, problog_flags/1, reset_problog_flags/0, problog_assert/1, problog_assert/2, problog_retractall/1, problog_statistics/2, problog_statistics/0, grow_atom_table/1, problog_exact_nested/3, problog_tabling_negated_synonym/2, problog_control/2, build_trie/2, build_trie/3, problog_infer/2, problog_infer/3, problog_infer_forest/2, write_bdd_struct_script/3, problog_bdd_forest/1, require/1, unrequire/1, bdd_files/2, delete_bdd_forest_files/1, recover_grounding_id/2, grounding_is_known/2, grounding_id/3, decision_fact/2, reset_non_ground_facts/0, '::'/2, probabilistic_fact/3, init_problog/1, problog_call/1, problog_infer_forest_supported/0, problog_bdd_forest_supported/0, problog_real_kbest/4, op( 550, yfx, :: ), op( 550, fx, ?:: ), op(1149, yfx, <-- ), op( 1150, fx, problog_table ), in_interval/3, below/2, above/2]). :- style_check(all). :- yap_flag(unknown,error). :- set_prolog_flag(to_chars_mode,quintus). % general yap modules :- use_module(library(charsio)). :- use_module(library(lists)). :- use_module(library(terms)). :- use_module(library(random)). % PM doesn't seem to be used! :- use_module(library(system)). :- use_module(library(rbtrees)). % PM doesn't seem to be used! :- use_module(library(ordsets), [list_to_ord_set/2, ord_insert/3, ord_union/3]). % problog related modules :- use_module('problog/variables'). :- use_module('problog/extlists'). :- use_module('problog/gflags', [flag_store/2]). :- use_module('problog/flags'). :- use_module('problog/print'). :- use_module('problog/os'). :- use_module('problog/tptree'). :- use_module('problog/tabling'). :- use_module('problog/sampling'). :- use_module('problog/intervals'). :- use_module('problog/mc_DNF_sampling'). :- catch(use_module('problog/ad_converter'),_,true). :- catch(use_module('problog/variable_elimination'),_,true). % op attaching probabilities to facts :- op( 550, yfx, :: ). :- op( 550, fx, ?:: ). % for annotated disjunctions % :- op(1149, yfx, <-- ). %%%%%%%%%%%%%%%%%%%%%%%% % control predicates on various levels %%%%%%%%%%%%%%%%%%%%%%%% % global over all inference methods, internal use only :- dynamic(problog_predicate/2). :- dynamic(problog_continuous_predicate/3). % global over all inference methods, exported :- dynamic(tunable_fact/2). :- dynamic(non_ground_fact/1). :- dynamic(continuous_fact/1). %:- dynamic(problog_dir/1). % global, manipulated via problog_control/2 :- dynamic(up/0). :- dynamic(limit/0). :- dynamic(mc/0). :- dynamic(remember/0). :- dynamic(exact/0). % Theo tabling :- dynamic(find_decisions/0). :- dynamic(internal_strategy/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 decisions :- dynamic(decision_fact/2). % for fact where the proabability is a variable :- dynamic(dynamic_probability_fact/1). :- dynamic(dynamic_probability_fact_extract/2). % for storing continuous parts of proofs (Hybrid ProbLog) :- dynamic([hybrid_proof/3, hybrid_proof/4]). :- dynamic(hybrid_proof_disjoint/4). % ProbLog files declare prob. facts as P::G % and this module provides the predicate X::Y to iterate over them :- multifile('::'/2). % directory where problogbdd executable is located % automatically set during loading -- assumes it is in same place as this file (problog.yap) :- getcwd(PD), set_problog_path(PD). %%%%%%%%%%%% % iterative deepening on minimal probabilities (delta, max, kbest): % - first threshold (not in log-space as only used to retrieve argument for init_threshold/1, which is also used with user-supplied argument) % - last threshold to ensure termination in case infinite search space (saved also in log-space for easy comparison with current values during search) % - factor used to decrease threshold for next level, NewMin=Factor*OldMin (saved also in log-space) %%%%%%%%%%%% :- initialization(( problog_define_flag(first_threshold, problog_flag_validate_indomain_0_1_open, 'starting threshold iterative deepening', 0.1, inference), problog_define_flag(last_threshold, problog_flag_validate_indomain_0_1_open, 'stopping threshold iterative deepening', 1e-30, inference, flags:last_threshold_handler), problog_define_flag(id_stepsize, problog_flag_validate_indomain_0_1_close, 'threshold shrinking factor iterative deepening', 0.5, inference, flags:id_stepsize_handler) )). %%%%%%%%%%%% % prune check stops derivations if they use a superset of facts already known to form a proof % (very) costly test, can be switched on/off here (This is obsolete as it is not included in implementation) %%%%%%%%%%%% :- initialization( problog_define_flag(prunecheck, problog_flag_validate_switch, 'stop derivations including all facts of known proof', off, inference) ). %%%%%%%%%%%% % max number of calls to probabilistic facts per derivation (to ensure termination) %%%%%%%%%%%% :- initialization( problog_define_flag(maxsteps, problog_flag_validate_posint, 'max. number of prob. steps per derivation', 1000, inference) ). %%%%%%%%%%%% % BDD timeout in seconds, used as option in BDD tool % files to write BDD script and pars % bdd_file overwrites bdd_par_file with matching extended name % if different name wanted, respect order when setting % save BDD information for the (last) lower bound BDD used during inference % produces three files named save_script, save_params, save_map % located in the directory given by problog_flag dir %%%%%%%%%%%% :- initialization(( % problog_define_flag(bdd_path, problog_flag_validate_directory, 'problogbdd directory', '.',bdd), problog_define_flag(bdd_time, problog_flag_validate_posint, 'BDD computation timeout in seconds', 60, bdd), problog_define_flag(save_bdd, problog_flag_validate_boolean, 'save BDD files for (last) lower bound', false, bdd), problog_define_flag(dynamic_reorder, problog_flag_validate_boolean, 'use dynamic re-ordering for BDD', true, bdd), problog_define_flag(bdd_static_order, problog_flag_validate_boolean, 'use a static order', false, bdd) )). %%%%%%%%%%%% % determine whether ProbLog outputs information (number of proofs, intermediate results, ...) % default was true, as otherwise problog_delta won't output intermediate bounds % default is false now, as dtproblog will flood the user with verbosity %%%%%%%%%%%% :- initialization( problog_define_flag(verbose, problog_flag_validate_boolean, 'output intermediate information', false,output) ). %%%%%%%%%%%% % determine whether ProbLog outputs proofs when adding to trie % default is false %%%%%%%%%%%% :- initialization( problog_define_flag(show_proofs, problog_flag_validate_boolean, 'output proofs', false,output) ). %%%%%%%%%%%% % Trie dump parameter for saving a file with the trie structure in the directory by problog_flag dir %%%%%%%%%%%% :- initialization( problog_define_flag(triedump, problog_flag_validate_boolean, 'generate file: trie_file containing the trie structure', false,output) ). %%%%%%%%%%%% % Default inference method %%%%%%%%%%%% :- initialization(problog_define_flag(inference, problog_flag_validate_dummy, 'default inference method', exact, inference)). %%%%%%%%%%%% % Tunable Facts %%%%%%%%%%%% :- initialization(problog_define_flag(tunable_fact_start_value,problog_flag_validate_dummy,'How to initialize tunable probabilities',uniform(0.1,0.9),learning_general,flags:learning_prob_init_handler)). problog_dir(PD):- problog_path(PD). %%%%%%%%%%%%%%%%%%%%%%%% % initialization of global parameters %%%%%%%%%%%%%%%%%%%%%%%% init_global_params :- %grow_atom_table(1000000), %%%%%%%%%%%% % working directory: all the temporary and output files will be located there % it assumes a subdirectory of the current working dir % on initialization, the current dir is the one where the user's file is located % should be changed to use temporary folder structure of operating system %%%%%%%%%%%% tmpnam(TempFolder), atomic_concat([TempFolder, '_problog'], TempProblogFolder), problog_define_flag(dir, problog_flag_validate_directory, 'directory for files', TempProblogFolder, output), problog_define_flag(bdd_par_file, problog_flag_validate_file, 'file for BDD variable parameters', example_bdd_probs, bdd, flags:working_file_handler), problog_define_flag(bdd_result, problog_flag_validate_file, 'file to store result calculated from BDD', example_bdd_res, bdd, flags:working_file_handler), problog_define_flag(bdd_file, problog_flag_validate_file, 'file for BDD script', example_bdd, bdd, flags:bdd_file_handler), problog_define_flag(static_order_file, problog_flag_validate_file, 'file for BDD static order', example_bdd_order, bdd, flags:working_file_handler), %%%%%%%%%%%% % montecarlo: recalculate current approximation after N samples % montecarlo: write log to this file %%%%%%%%%%%% problog_define_flag(mc_logfile, problog_flag_validate_file, 'logfile for montecarlo', 'log.txt', mcmc, flags:working_file_handler), check_existance('problogbdd'). % 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) :- assertz(X). problog_control(off,X) :- retractall(X). problog_control(check,X) :- call(X). reset_control :- problog_control(off,up), problog_control(off,mc), problog_control(off,limit), % problog_control(off,exact), problog_control(off,remember). :- initialization(reset_control). grow_atom_table(N):- generate_atoms(N, 0), garbage_collect_atoms. generate_atoms(N, N):-!. generate_atoms(N, A):- NA is A + 1, atomic_concat([theo, A], _Atom), generate_atoms(N, NA). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 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 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% term_expansion_intern(A, B, C):- catch(term_expansion_intern_ad(A, B, C), _, false). % converts ?:: prefix to ? :: infix, as handled by other clause term_expansion_intern((Annotation::Fact), Module, ExpandedClause) :- Annotation == '?', term_expansion_intern((? :: Fact :- true), Module, ExpandedClause). % handles decision clauses term_expansion_intern((Annotation :: Head :- Body), Module, problog:ExpandedClause) :- ( Annotation == '?' -> % It's a decision with a body copy_term((Head,Body),(HeadCopy,_BodyCopy)), functor(Head, Functor, Arity), atomic_concat([problog_,Functor],LongFunctor), Head =.. [Functor|Args], append(Args,[LProb],LongArgs), probclause_id(ID), ProbFactHead =.. [LongFunctor,ID|LongArgs], assertz(decision_fact(ID,Head)), ExpandedClause = (ProbFactHead :- user:Body, (problog_control(check,internal_strategy) -> dtproblog:strategy_log(ID,Head,LProb) ; LProb = '?' ) ), assertz(dynamic_probability_fact(ID)), assertz((dynamic_probability_fact_extract(HeadCopy,P_New) :- dtproblog:strategy(ID,HeadCopy,P_New) )), (ground(Head) -> true ; assertz(non_ground_fact(ID)) ), problog_predicate(Functor, Arity, LongFunctor, Module) ; % If it has a body, it's not supported (Body == true -> % format('Expanding annotated fact ~q :: ~q :- ~q in other clause.~n',[Annotation,Head,Body]), fail ; throw(error('We do not support annotated clauses (yet)!', (Annotation :: Head :- Body))) ) ). /* 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, !. */ % handles probabilistic facts term_expansion_intern(P :: Goal,Module,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], ( (nonvar(P), P = t(TrueProb)) -> ( assertz(tunable_fact(ID,TrueProb)), sample_initial_value_for_tunable_fact(LProb) ); ( ground(P) -> EvalP is P, % allows one to use ground arithmetic expressions as probabilities LProb is log(P), assert_static(prob_for_id(ID,EvalP,LProb)); % Prob is fixed -- assert it for quick retrieval ( % 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), assertz(dynamic_probability_fact(ID)), assertz(dynamic_probability_fact_extract(Goal_Copy,P_Copy)) ) ) ), ( ground(Goal) -> true; assertz(non_ground_fact(ID)) ), problog_predicate(Name, Arity, ProblogName,Module). sample_initial_value_for_tunable_fact(LogP) :- problog_flag(tunable_fact_start_value,Initializer), ( Initializer=uniform(Low,High) -> ( Spread is High-Low, random(Rand), P1 is Rand*Spread+Low, % security check, to avoid log(0) ( P1>0 -> P=P1; P=0.5 ) ); ( number(Initializer) -> P=Initializer; throw(unkown_probability_initializer(Initializer)) ) ), LogP is log(P). % Hybrid ProbLog stuff is_valid_gaussian(X) :- compound(X), X=gaussian(Mu,Sigma), ( ((number(Mu),number(Sigma));(Mu=t(_),Sigma=t(_))) -> true; throw(invalid_gaussian(X)) ). :- multifile(user:term_expansion/1). user:term_expansion(Goal, problog:ProbFact) :- compound(Goal), Goal=..[Name|Args], once( (nth(Pos,Args,GaussianArg),is_valid_gaussian(GaussianArg)) ), %Goal contains a Gaussian, there is some work to do ( % check for a second Gaussian (nth(Pos2,Args,GaussianArg2),Pos2\=Pos,is_valid_gaussian(GaussianArg2)) -> ( format(user_error,'We only support continous atoms with at most one Gaussian inside.~n',[]), format(user_error,'Your program contains the atom ~w with more than one.~n',[]), throw(unsupported_multivariate_gaussian(Goal)) ); true ), functor(Goal, Name, Arity), atomic_concat([problogcontinuous_,Name],ProblogName), probclause_id(ID), GaussianArg=gaussian(Mu_Arg,Sigma_Arg), % is it a tunable fact? ( (number(Mu_Arg),number(Sigma_Arg)) -> NewArgs=Args; ( Mu_Random is 0.1, % random*4-2, Sigma_Random is 0.4, % random*2+0.5, nth(Pos,Args,_,KeepArgs), nth(Pos,NewArgs,gaussian(Mu_Random,Sigma_Random),KeepArgs), assertz(tunable_fact(ID,gaussian(Mu_Arg,Sigma_Arg))) ) ), ProbFact =.. [ProblogName,ID|NewArgs], ( ground(Goal) -> true; assertz(non_ground_fact(ID)) ), assertz(continuous_fact(ID)), problog_continuous_predicate(Name, Arity, Pos,ProblogName). % introduce wrapper clause if predicate seen first time problog_continuous_predicate(Name, Arity,ContinuousArgumentPosition,_) :- problog_continuous_predicate(Name, Arity,OldContinuousArgumentPosition), !, ( ContinuousArgumentPosition=OldContinuousArgumentPosition -> true; ( format(user_error,'Continuous predicates of the same name and arity must ',[]), format(user_error,'have the continuous argument all at the same position.~n',[]), format(user_error,'Your program contains the predicate ~q/~q. There are ',[]), format(user_error,'atoms which have the continuous argument at position ',[]), format(user_error,'~q and other have it at ~q.',[Name,Arity,OldContinuousArgumentPosition,ContinuousArgumentPosition]), throw(continuous_argument(not_unique_position)) ) ). problog_continuous_predicate(Name, Arity, ContinuousArgumentPosition, ProblogName) :- LBefore is ContinuousArgumentPosition-1, LAfter is Arity-ContinuousArgumentPosition, length(ArgsBefore,LBefore), length(ArgsAfter,LAfter), append(ArgsBefore,[(ID,ID2,GaussianArg)|ArgsAfter],Args), append(ArgsBefore,[GaussianArg|ArgsAfter],ProbArgs), OriginalGoal =.. [Name|Args], ProbFact =.. [ProblogName,ID|ProbArgs], prolog_load_context(module,Mod), assertz( (Mod:OriginalGoal :- ProbFact, % continuous facts always get a grounding ID, even when they are actually ground % this simplifies the BDD script generation non_ground_fact_grounding_id(ProbFact,Ground_ID), atomic_concat([ID,'_',Ground_ID],ID2), add_continuous_to_proof(ID,ID2) )), assertz(problog_continuous_predicate(Name, Arity,ContinuousArgumentPosition)), ArityPlus1 is Arity+1, dynamic(problog:ProblogName/ArityPlus1). in_interval(ID,Low,High) :- number(Low), number(High), Low non_ground_fact_grounding_id(Goal,G_ID), atomic_concat([ID,'_',G_ID],ID2) ; ID2=ID ). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % What to do when prolog tries to prove a problog fact %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% prove_problog_fact(ClauseID,GroundID,Prob) :- (problog_control(check,find_decisions) -> signal_decision(ClauseID,GroundID) ; (Prob = '?' -> add_to_proof(GroundID,0) % 0 is log(1)! ; % Checks needed for LeDTProbLog (Prob = always -> % Always true, do not add to trie true ; (Prob = never -> % Always false, do not add to trie fail ; % something in between, add to proof ProbEval is Prob, add_to_proof(GroundID,ProbEval) ) ) ) ). prove_problog_fact_negated(ClauseID,GroundID,Prob) :- (problog_control(check,find_decisions) -> signal_decision(ClauseID,GroundID) ; (Prob = '?' -> add_to_proof_negated(GroundID,-inf) % 0 is log(1)! ; % Checks needed for LeDTProbLog (Prob = always -> % Always true, do not add to trie fail ; (Prob = never -> % Always false, do not add to trie true ; % something in between, add to proof ProbEval is Prob, add_to_proof_negated(GroundID,ProbEval) ) ) ) ). % generate next global identifier :- initialization(nb_setval(probclause_counter,0)). probclause_id(ID) :- nb_getval(probclause_counter,ID), !, C1 is ID+1, nb_setval(probclause_counter,C1), !. 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), assertz(grounding_is_known(Goal,ID)) ) ). reset_non_ground_facts :- (required(keep_ground_ids) -> true ; nb_setval(non_ground_fact_grounding_id_counter,0), retractall(grounding_is_known(_,_)) ). :- initialization(reset_non_ground_facts). % backtrack over all probabilistic facts % must come before term_expansion P::Goal :- probabilistic_fact(P,Goal,_). % backtrack over all probabilistic facts probabilistic_fact(P2,Goal,ID) :- ( ground(Goal) -> ( Goal =.. [F|Args], atomic_concat('problog_',F,F2), append([ID|Args],[P],Args2), Goal2 =..[F2|Args2], length(Args2,N), current_predicate(F2/N), call(Goal2), number(P), P2 is exp(P) ); ( get_internal_fact(ID,ProblogTerm,_ProblogName,_ProblogArity), ProblogTerm =.. [F,_ID|Args], append(Args2,[P],Args), name(F,[_p,_r,_o,_b,_l,_o,_g,_|F2Chars]), name(F2,F2Chars), Goal =.. [F2|Args2], ( dynamic_probability_fact(ID) -> P2=p; P2 is exp(P) ) ) ). % generates unique IDs for proofs proof_id(ID) :- nb_getval(problog_proof_id,ID), ID2 is ID+1, nb_setval(problog_proof_id,ID2). reset_problog_proof_id :- nb_setval(problog_proof_id,0). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % access/update the probability of ID's fact % hardware-access version: naively scan all problog-predicates (except if prob is recorded in static database), % 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) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % using a dummy for the static prob database is more efficient than checking for current_predicate prob_for_id(dummy,dummy,dummy). get_fact_probability(A, Prob) :- ground(A), \+ number(A), name(A, A_Codes), once(append(Part1, [95|Part2], A_Codes)), % 95 = '_' number_codes(ID, Part1), !, % let's check whether Part2 contains an 'l' (l=low) (member(108, Part2) -> fail ; number_codes(Grounding_ID, Part2), (dynamic_probability_fact(ID) -> grounding_is_known(Goal, Grounding_ID), dynamic_probability_fact_extract(Goal, Prob) ; get_fact_probability(ID, Prob) ) ). get_fact_probability(ID,Prob) :- ground(ID), prob_for_id(ID,Prob,_), !. 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), (Log = '?' -> throw(error('Why do you want to know the probability of a decision?')) %fail ; Prob is exp(Log) ). get_fact_log_probability(ID,Prob) :- ground(ID), prob_for_id(ID,_,Prob),!. get_fact_log_probability(ID,Prob) :- ( ground(ID) -> get_internal_fact(ID,ProblogTerm,_ProblogName,ProblogArity),! ; get_internal_fact(ID,ProblogTerm,_ProblogName,ProblogArity) ), arg(ProblogArity,ProblogTerm,Prob), Prob \== '?'. get_fact_log_probability(ID,Prob) :- get_fact_probability(ID,Prob1), Prob is log(Prob1). 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], assertz(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). get_continuous_fact_parameters(ID,Parameters) :- ( ground(ID) -> get_internal_continuous_fact(ID,ProblogTerm,_ProblogName,ProblogArity,ContinuousPos),! ; get_internal_continuous_fact(ID,ProblogTerm,_ProblogName,ProblogArity,ContinuousPos) ), InternalPos is ContinuousPos+1, arg(InternalPos,ProblogTerm,Parameters). get_internal_continuous_fact(ID,ProblogTerm,ProblogName,ProblogArity,ContinuousPos) :- problog_continuous_predicate(Name,Arity,ContinuousPos), atomic_concat([problogcontinuous_,Name],ProblogName), ProblogArity is Arity+1, functor(ProblogTerm,ProblogName,ProblogArity), arg(1,ProblogTerm,ID), call(ProblogTerm). set_continuous_fact_parameters(ID,Parameters) :- get_internal_continuous_fact(ID,ProblogTerm,ProblogName,_ProblogArity,ContinuousPos), retract(ProblogTerm), ProblogTerm =.. [ProblogName|ProblogTermArgs], nth0(ContinuousPos,ProblogTermArgs,_,KeepArgs), nth0(ContinuousPos,NewProblogTermArgs,Parameters,KeepArgs), NewProblogTerm =.. [ProblogName|NewProblogTermArgs], assertz(NewProblogTerm). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % writing those facts with learnable parameters to File %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% export_facts(Filename) :- open(Filename,'write',Handle), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( % go over all probabilistic facts P::Goal, format(Handle,'~w :: ~q.~n',[P,Goal]), fail; % go to next prob. fact true ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( % go over all continuous facts continuous_fact(ID), get_continuous_fact_parameters(ID,Param), format(Handle,'~q. % ~q~n',[Param,ID]), fail; % go to next cont. fact true ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% close(Handle). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 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 \+ open_end_memberchk(not(ID),IDs), %%%% Bernd, changes for negated ground facts (MaxSteps =< 0 -> fail ; (open_end_memberchk(ID, IDs) -> %Theo true ; open_end_add(ID, IDs, NIDs), %Theo % \+ prune_check(NIDs, Trie_Completed_Proofs), multiply_probabilities(CurrentP, Prob, NProb), (NProb < CurrentThreshold -> upper_bound(NIDs), fail ; b_setval(problog_probability, NProb), b_setval(problog_current_proof, NIDs) ) ), Steps is MaxSteps - 1, b_setval(problog_steps, Steps) ) ). %%%% 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), \+ open_end_memberchk(ID, IDs), (MaxSteps =< 0 -> fail ; (open_end_memberchk(not(ID), IDs) -> true ; open_end_add(not(ID), IDs, NIDs), %Theo % \+ prune_check(NIDs, Trie_Completed_Proofs), InverseProb is log(1 - exp(Prob)), multiply_probabilities(CurrentP, InverseProb, NProb), (NProb < CurrentThreshold -> upper_bound(NIDs), %% checkme fail ; b_setval(problog_probability, NProb), b_setval(problog_current_proof, NIDs) ) ), Steps is MaxSteps - 1, b_setval(problog_steps, Steps) ) ). %%%% Bernd, changes for negated ground facts %Hybrid add_continuous_to_proof(ID,GroundID) :- b_getval(problog_continuous_facts_used,Facts), ( memberchk((ID,GroundID),Facts) -> true; ( b_setval(problog_continuous_facts_used,[(ID,GroundID)|Facts]), atomic_concat([interval,'_',GroundID],Key), b_setval(Key,all) ) ). % if in monte carlo mode ... % (a) for ground facts (ID is number): check array to see if it can be used montecarlo_check(ID) :- number(ID), !, array_element(mc_sample,ID,V), ( V == 1 -> true ; V == 2 -> fail ; new_sample(ID) ). % (b) for non-ground facts (ID is FactID_GroundingID): check database of groundings in current sample montecarlo_check(ComposedID) :- % split_grounding_id(ComposedID,ID,GID), recorded(mc_true,problog_mc_id(ComposedID),_), !. montecarlo_check(ComposedID) :- % split_grounding_id(ComposedID,ID,GID), recorded(mc_false,problog_mc_id(ComposedID),_), !, fail. % (c) for unknown groundings of non-ground facts: generate a new sample (decompose the ID first) montecarlo_check(ID) :- name(ID,IDN), recover_number(IDN,FactIDName), name(FactID,FactIDName), new_sample_nonground(ID,FactID). % sampling from ground fact: set array value to 1 (in) or 2 (out) new_sample(ID) :- get_fact_probability(ID,Prob), problog_random(R), R get_fact(ID,Fact), split_grounding_id(ComposedID,ID,GID), grounding_is_known(Fact,GID), dynamic_probability_fact_extract(Fact,Prob) ; get_fact_probability(ID,Prob) ), problog_random(R), (R < Prob -> recorda(mc_true,problog_mc_id(ComposedID),_) ; recorda(mc_false,problog_mc_id(ComposedID),_), fail ). % new_sample_nonground(ComposedID,_ID) :- % recorda(mc_false,problog_mc_id(ComposedID),_), % fail. split_grounding_id(Composed,Fact,Grounding) :- name(Composed,C), split_g_id(C,F,G), name(Fact,F), name(Grounding,G). split_g_id([95|Grounding],[],Grounding) :- !. split_g_id([A|B],[A|FactID],GroundingID) :- split_g_id(B,FactID,GroundingID). % 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), nb_getval(problog_stopped_proofs, Trie_Stopped_Proofs), open_end_close_end(List, R), % (prune_check(R, Trie_Stopped_Proofs) -> true; insert_ptree(R, Trie_Stopped_Proofs)). insert_ptree(R, Trie_Stopped_Proofs). 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 % it resets control flags, method specific values to be set afterwards! init_problog(Threshold) :- reset_problog_proof_id, reset_non_ground_facts, reset_control, LT is log(Threshold), b_setval(problog_probability, 0.0), b_setval(problog_current_proof, []), nb_setval(problog_threshold, LT), problog_flag(maxsteps,MaxS), init_tabling, problog_var_clear_all, b_setval(problog_steps, MaxS), b_setval(problog_continuous_facts_used,[]), retractall(hybrid_proof(_,_,_)), retractall(hybrid_proof(_,_,_,_)), retractall(hybrid_proof_disjoint(_,_,_,_)). % 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, Trie) :- problog_flag(prunecheck, on), prune_check_ptree(Proof, Trie). % 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: Trie the 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 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% :- initialization(( problog_var_define(sld_time, times, time, messages('SLD resolution', ':', ' ms')), problog_var_define(bdd_script_time, times, time, messages('Generating BDD script', ':', ' ms')), problog_var_define(bdd_generation_time, times, time, messages('Constructing BDD', ':', ' ms')), problog_var_define(trie_statistics, memory, untyped, messages('Trie usage', ':', '')), problog_var_define(probability, result, number, messages('Probabilty', ' = ', '')), problog_var_define(bdd_script_time(Method), times, time, messages('Generating BDD script '(Method), ':', ' ms')), problog_var_define(bdd_generation_time(Method), times, time, messages('Constructing BDD '(Method), ':', ' ms')), problog_var_define(probability(Method), result, number, messages('Probabilty '(Method), ' = ', '')), problog_var_define(trie_statistics(Method), memory, untyped, messages('Trie usage '(Method), ':', '')), problog_var_define(dbtrie_statistics(Method), memory, untyped, messages('Depth Breadth Trie usage '(Method), ':', '')), problog_var_define(db_trie_opts_performed(Method), memory, untyped, messages('Optimisations performed '(Method), ':', '')), problog_var_define(variable_elimination_time, times, time, messages('Variable Elimination', ':', ' ms')), problog_var_define(variable_elimination_stats, memory, untyped, messages('Variable Elimination', ':', '')) )). problog_statistics(Stat, Result):- problog_var_defined(Stat), problog_var_is_set(Stat), problog_var_get(Stat, Result). generate_order_by_prob_fact_appearance(Order, FileName):- open(FileName, 'write', Stream), forall(member(PF, Order), ( ptree:get_var_name(PF, Name), format(Stream, "@~w~n", [Name]))), /* findall(_, (recorded(variable_elimination, prob_fact(PF, _), _), ptree:get_var_name(PF, Name), format(Stream, "@~w~n", [Name])), _),*/ close(Stream). get_order(Trie, Order):- findall(List, ptree:traverse_ptree(Trie, List), Proofs), flatten(Proofs, ProbFacts), remove_duplicates(ProbFacts, Order). eval_dnf(OriTrie1, Prob, Status) :- % Check whether we use Hybrid ProbLog ( hybrid_proof(_,_,_) -> ( % Yes! run the disjoining stuff retractall(hybrid_proof_disjoint(_,_,_,_)), disjoin_hybrid_proofs, init_ptree(OriTrie), % use this as tmp ptree %%%%%%%%%%%%%%%%%%%%% ( % go over all stored proofs enum_member_ptree(List,OriTrie1), ( List=[_|_] -> Proof=List; Proof=[List] ), ( select(continuous(ProofID),Proof,Rest) -> ( % this proof is using continuous facts all_hybrid_subproofs(ProofID,List2), append(Rest,List2,NewProof), insert_ptree(NewProof,OriTrie) ); insert_ptree(Proof,OriTrie) ), fail; true ) %%%%%%%%%%%%%%%%%%%%% ) ; % Nope, just pass on the Trie OriTrie=OriTrie1 ), ((problog_flag(variable_elimination, true), nb_getval(problog_nested_tries, false)) -> statistics(walltime, _), trie_check_for_and_cluster(OriTrie), statistics(walltime, [_, VariableEliminationTime]), trie_replace_and_cluster(OriTrie, Trie), problog_var_set(variable_elimination_time, VariableEliminationTime), variable_elimination_stats(Clusters, OrigPF, CompPF), problog_var_set(variable_elimination_stats, compress(Clusters, OrigPF, CompPF)), clean_up ; Trie = OriTrie ), (problog_flag(bdd_static_order, true) -> get_order(Trie, Order), problog_flag(static_order_file, SOFName), convert_filename_to_working_path(SOFName, SOFileName), generate_order_by_prob_fact_appearance(Order, SOFileName) ; true ), ptree:trie_stats(Memory, Tries, Entries, Nodes), (nb_getval(problog_nested_tries, false) -> ptree:trie_usage(Trie, TEntries, TNodes, TVirtualNodes), problog_var_set(trie_statistics, tries(memory(Memory), tries(Tries), entries(TEntries), nodes(TNodes), virtualnodes(TVirtualNodes))) ; problog_var_set(trie_statistics, tries(memory(Memory), tries(Tries), entries(Entries), nodes(Nodes))) ), (problog_flag(triedump, true) -> convert_filename_to_working_path(trie_file, TrieFile), tell(TrieFile), print_nested_ptree(Trie), flush_output, told, tell(user_output) ; true ), nb_getval(problog_completed_proofs, Trie_Completed_Proofs), ((Trie = Trie_Completed_Proofs, problog_flag(save_bdd, true)) -> problog_control(on, remember) ; problog_control(off, remember) ), problog_flag(bdd_file, BDDFileFlag), convert_filename_to_working_path(BDDFileFlag, BDDFile), problog_flag(bdd_par_file, BDDParFileFlag), convert_filename_to_working_path(BDDParFileFlag, BDDParFile), % old reduction method doesn't support nested tries ((problog_flag(use_old_trie, true), nb_getval(problog_nested_tries, false)) -> statistics(walltime, _), (problog_control(check, remember) -> bdd_ptree_map(Trie, BDDFile, BDDParFile, Mapping), convert_filename_to_working_path(save_map, MapFile), tell(MapFile), format('mapping(~q).~n', [Mapping]), flush_output, told ; bdd_ptree(Trie, BDDFile, BDDParFile) ), statistics(walltime, [_, ScriptGenerationTime]), problog_var_set(bdd_script_time, ScriptGenerationTime), statistics(walltime, _), execute_bdd_tool(BDDFile, BDDParFile, Prob_old, Status_old), statistics(walltime,[_, BDDGenerationTime]), (Status_old = ok -> problog_var_set(bdd_generation_time, BDDGenerationTime), problog_var_set(probability, Prob_old) ; problog_var_set(bdd_generation_time, fail), problog_var_set(probability, fail) ) ; true ), % naive method with nested trie support but not loops ((problog_flag(use_naive_trie, true); (problog_flag(use_old_trie, true), nb_getval(problog_nested_tries, true))) -> statistics(walltime, _), % atomic_concat([BDDFile, '_naive'], BDDFile_naive), BDDFile = BDDFile_naive, nested_ptree_to_BDD_script(Trie, BDDFile_naive, BDDParFile), statistics(walltime, [_, ScriptGenerationTime_naive]), problog_var_set(bdd_script_time(naive), ScriptGenerationTime_naive), statistics(walltime, _), execute_bdd_tool(BDDFile_naive, BDDParFile, Prob_naive, Status_naive), statistics(walltime,[_, BDDGenerationTime_naive]), (Status_naive = ok -> problog_var_set(bdd_generation_time(naive), BDDGenerationTime_naive), problog_var_set(probability(naive), Prob_naive) ; problog_var_set(bdd_generation_time(naive), fail), problog_var_set(probability(naive), fail) ) ; true ), % problog_statistics, % print_nested_ptree(Trie), % findall(_,(problog_chktabled(_ID, _T), writeln(problog_chktabled(_ID, _T))),_), % reduction method with depth_breadth trie support problog_flag(db_trie_opt_lvl, ROptLevel), problog_flag(db_min_prefix, MinPrefix), (problog_flag(compare_opt_lvl, true) -> generate_ints(0, ROptLevel, Levels) ; Levels = [ROptLevel] ), forall(member(OptLevel, Levels), ( (problog_flag(use_db_trie, true) -> tries:trie_db_opt_min_prefix(MinPrefix), statistics(walltime, _), % atomic_concat([BDDFile, '_builtin_', OptLevel], BDDFile_builtin), BDDFile = BDDFile_builtin, (nb_getval(problog_nested_tries, false) -> trie_to_bdd_trie(Trie, DBTrie, BDDFile_builtin, OptLevel, BDDParFile) ; nested_trie_to_bdd_trie(Trie, DBTrie, BDDFile_builtin, OptLevel, BDDParFile) ), atomic_concat(['builtin_', OptLevel], Builtin), ptree:trie_stats(DBMemory, DBTries, DBEntries, DBNodes), FM is DBMemory - Memory, FT is DBTries - Tries, FE is DBEntries - Entries, FN is DBNodes - Nodes, problog_var_set(dbtrie_statistics(Builtin), tries(memory(FM), tries(FT), entries(FE), nodes(FN))), delete_ptree(DBTrie), statistics(walltime, [_, ScriptGenerationTime_builtin]), problog_var_set(bdd_script_time(Builtin), ScriptGenerationTime_builtin), statistics(walltime, _), execute_bdd_tool(BDDFile_builtin, BDDParFile, Prob_builtin, Status_builtin), statistics(walltime,[_, BDDGenerationTime_builtin]), ptree_db_trie_opt_performed(LVL1, LVL2, LV3), problog_var_set(db_trie_opts_performed(Builtin), opt_perform(LVL1, LVL2, LV3)), (Status_builtin = ok -> problog_var_set(bdd_generation_time(Builtin), BDDGenerationTime_builtin), problog_var_set(probability(Builtin), Prob_builtin) ; problog_var_set(bdd_generation_time(Builtin), fail), problog_var_set(probability(Builtin), fail) ) ; true ) )), % decomposition method (problog_flag(use_dec_trie, true) -> statistics(walltime, _), % atomic_concat([BDDFile, '_dec'], BDDFile_dec), BDDFile = BDDFile_dec, ptree_decomposition(Trie, BDDFile_dec, BDDParFile), statistics(walltime, [_, ScriptGenerationTime_dec]), problog_var_set(bdd_script_time(dec), ScriptGenerationTime_dec), statistics(walltime, _), execute_bdd_tool(BDDFile_dec, BDDParFile, Prob_dec, Status_dec), statistics(walltime,[_, BDDGenerationTime_dec]), (Status_dec = ok -> problog_var_set(bdd_generation_time(dec), BDDGenerationTime_dec), problog_var_set(probability(dec), Prob_dec) ; problog_var_set(bdd_generation_time(dec), fail), problog_var_set(probability(dec), fail) ) ; true ), (problog_control(check, remember) -> convert_filename_to_working_path('save_script', SaveBDDFile), rename_file(BDDFile, SaveBDDFile), convert_filename_to_working_path('save_params', SaveBDDParFile), rename_file(BDDParFile, SaveBDDParFile) ; true ), problog_control(off, remember), (var(Status_old)-> (var(Status_naive)-> (var(Status_dec) -> atomic_concat('builtin_', ROptLevel, ProbStat), problog_statistics(probability(ProbStat), ProbB), (ProbB = fail -> Status = timeout ; Prob = ProbB, Status = ok ) ; Prob = Prob_dec, Status = Status_dec ) ; Prob = Prob_naive, Status = Status_naive ) ; Prob = Prob_old, Status = Status_old ), (Trie =\= OriTrie -> delete_ptree(Trie) ; true ). generate_ints(End, End, [End]). generate_ints(Start, End, [Start|Rest]):- Start < End, Current is Start + 1, generate_ints(Current, End, Rest). execute_bdd_tool(BDDFile, BDDParFile, Prob, Status):- problog_flag(bdd_time, BDDTime), problog_flag(bdd_result, ResultFileFlag), (problog_flag(dynamic_reorder, true) -> ParamD = '' ; ParamD = ' -dreorder' ), (problog_flag(bdd_static_order, true) -> problog_flag(static_order_file, FileName), convert_filename_to_working_path(FileName, SOFileName), atomic_concat([ParamD, ' -sord ', SOFileName], Param) ; Param = ParamD ), convert_filename_to_problog_path('problogbdd', ProblogBDD), convert_filename_to_working_path(ResultFileFlag, ResultFile), atomic_concat([ProblogBDD, Param,' -l ', BDDFile, ' -i ', BDDParFile, ' -m p -t ', BDDTime, ' > ', ResultFile], Command), shell(Command, Return), (Return =\= 0 -> Status = timeout ; see(ResultFile), read(probability(Prob)), seen, delete_file(ResultFile), Status = ok ). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 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, _, _, _) :- init_problog_threshold(Threshold), problog_control(on,up), problog_call(Goal), add_solution, fail. problog_threshold(_, _, LP, UP, Status) :- compute_bounds(LP, UP, Status). init_problog_threshold(Threshold) :- init_ptree(Trie_Completed_Proofs), nb_setval(problog_completed_proofs, Trie_Completed_Proofs), init_ptree(Trie_Stopped_Proofs), nb_setval(problog_stopped_proofs, Trie_Stopped_Proofs), init_problog(Threshold). add_solution :- % get the probabilistic facts used in this proof b_getval(problog_current_proof, IDs), (IDs == [] -> R = []; open_end_close_end(IDs, R)), % get the continuous facts used in this proof % (Hybrid ProbLog b_getval(problog_continuous_facts_used,Cont_IDs), ( Cont_IDs == [] -> Continuous=[]; ( proof_id(ProofID), collect_all_intervals(Cont_IDs,ProofID,AllIntervals), ( AllIntervals==[] -> Continuous=[]; ( Continuous=[continuous(ProofID)], assertz(hybrid_proof(ProofID,Cont_IDs,AllIntervals)) ) ) ) ), % we have both, no add it to the trie nb_getval(problog_completed_proofs, Trie_Completed_Proofs), append(R,Continuous,Final), ( Final==[] -> insert_ptree(true, Trie_Completed_Proofs); insert_ptree(Final, Trie_Completed_Proofs) ). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% collect_all_intervals([],_,[]). collect_all_intervals([(ID,GroundID)|T],ProofID,[Interval|T2]) :- atomic_concat([interval,'_',GroundID],Key), b_getval(Key,Interval), Interval \= all, % we do not need to store continuous % variables with domain [-oo,oo] (they have probability 1) !, assertz(hybrid_proof(ProofID,ID,GroundID,Interval)), collect_all_intervals(T,ProofID,T2). collect_all_intervals([_|T],ProofID,T2) :- collect_all_intervals(T,ProofID,T2). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% all_hybrid_subproofs(ProofID,List) :- findall((ID,GroundID,Intervals),hybrid_proof_disjoint(ProofID,ID,GroundID,Intervals),All), generate_all_proof_combinations(All,List). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% generate_all_proof_combinations([],[]). generate_all_proof_combinations([(_ID,GroundID,Intervals)|T],Result) :- member((Interval,Tail),Intervals), intervals_encode(Interval,IntervalEncoded), atomic_concat([GroundID,IntervalEncoded],FullID), encode_tail(Tail,GroundID,TailEncoded), append([FullID|TailEncoded],T2,Result), generate_all_proof_combinations(T,T2). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% encode_tail([],_,[]). encode_tail([A|T],ID,[not(FullID)|T2]) :- intervals_encode(A,AEncoded), atomic_concat([ID,AEncoded],FullID), encode_tail(T,ID,T2). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% disjoin_hybrid_proofs :- % collect all used continuous facts findall(GroundID,hybrid_proof(_,_,GroundID,_),IDs), sort(IDs,IDsSorted), disjoin_hybrid_proofs(IDsSorted). disjoin_hybrid_proofs([]). disjoin_hybrid_proofs([GroundID|T]) :- findall(Interval,hybrid_proof(_,_,GroundID,Interval),Intervals), intervals_partition(Intervals,Partition), % go over all proofs where this fact occurs ( hybrid_proof(ProofID,ID,GroundID,Interval), intervals_disjoin(Interval,Partition,PInterval), assertz(hybrid_proof_disjoint(ProofID,ID,GroundID,PInterval)), fail; % go to next proof true ), disjoin_hybrid_proofs(T). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % End Hybrid compute_bounds(LP, UP, Status) :- nb_getval(problog_completed_proofs, Trie_Completed_Proofs), nb_getval(problog_stopped_proofs, Trie_Stopped_Proofs), eval_dnf(Trie_Completed_Proofs, LP, StatusLow), (StatusLow \== ok -> Status = StatusLow ; merge_ptree(Trie_Completed_Proofs, Trie_Stopped_Proofs, Trie_All_Proofs), nb_setval(problog_all_proofs, Trie_All_Proofs), eval_dnf(Trie_All_Proofs, UP, Status)), delete_ptree(Trie_Completed_Proofs), delete_ptree(Trie_Stopped_Proofs), delete_ptree(Trie_All_Proofs). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 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, _, _) :- init_problog_low(Threshold), problog_control(off, up), statistics(walltime, _), problog_call(Goal), add_solution, fail. problog_low(_, _, LP, Status) :- statistics(walltime, [_,E]), %theo problog_var_set(sld_time, E), nb_getval(problog_completed_proofs, Trie_Completed_Proofs), eval_dnf(Trie_Completed_Proofs, LP, Status), (problog_flag(verbose, true)-> problog_statistics ; true ), delete_ptree(Trie_Completed_Proofs), (problog_flag(retain_tables, true) -> retain_tabling; true), clear_tabling. init_problog_low(Threshold) :- init_ptree(Trie_Completed_Proofs), nb_setval(problog_completed_proofs, Trie_Completed_Proofs), 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_flag(first_threshold,InitT), init_problog_delta(InitT,Delta), problog_control(on,up), problog_delta_id(Goal,Status), nb_getval(problog_completed_proofs, Trie_Completed_Proofs), nb_getval(problog_stopped_proofs, Trie_Stopped_Proofs), delete_ptree(Trie_Completed_Proofs), delete_ptree(Trie_Stopped_Proofs), (retract(low(_,Low)) -> true; true), (retract(up(_,Up)) -> true; true). init_problog_delta(Threshold,Delta) :- retractall(low(_,_)), retractall(up(_,_)), retractall(stopDiff(_)), init_ptree(Trie_Completed_Proofs), nb_setval(problog_completed_proofs, Trie_Completed_Proofs), init_ptree(Trie_Stopped_Proofs), nb_setval(problog_stopped_proofs, Trie_Stopped_Proofs), assertz(low(0,0.0)), assertz(up(0,1.0)), assertz(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), nb_getval(problog_completed_proofs, Trie_Completed_Proofs), nb_getval(problog_stopped_proofs, Trie_Stopped_Proofs), count_ptree(Trie_Completed_Proofs, NProofs), count_ptree(Trie_Stopped_Proofs, 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(Trie_Stopped_Proofs), init_ptree(New_Trie_Stopped_Proofs), nb_setval(problog_stopped_proofs, New_Trie_Stopped_Proofs) ), (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, nb_getval(problog_completed_proofs, Trie_Completed_Proofs), eval_dnf(Trie_Completed_Proofs,P,Status), (Status = ok -> retract(low(_,_)), assertz(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), assertz(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, nb_getval(problog_completed_proofs, Trie_Completed_Proofs), nb_getval(problog_stopped_proofs, Trie_Stopped_Proofs), merge_ptree(Trie_Completed_Proofs,Trie_Stopped_Proofs,Trie_All_Proofs), nb_setval(problog_all_proofs, Trie_All_Proofs), eval_dnf(Trie_All_Proofs,UpP,StatusUp), delete_ptree(Trie_All_Proofs), (StatusUp = ok -> retract(up(_,_)), assertz(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_flag(first_threshold,InitT), init_problog_max(InitT), problog_control(off,up), problog_max_id(Goal, Prob, FactIDs),% theo todo ( FactIDs = [_|_] -> get_fact_list(FactIDs, Facts); Facts = FactIDs). init_problog_max(Threshold) :- retractall(max_probability(_)), retractall(max_proof(_)), assertz(max_probability(-999999)), assertz(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), open_end_close_end(IDs, R), retractall(max_proof(_)), assertz(max_proof(R)), nb_setval(problog_threshold, CurrP), retractall(max_probability(_)), assertz(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_flag(dir, InternWorkingDir), problog_flag(bdd_file, InternBDDFlag), problog_flag(bdd_par_file, InternParFlag), split_path_file(BDDFile, WorkingDir, BDDFileName), split_path_file(ParamFile, _WorkingDir, ParamFileName), flag_store(dir, WorkingDir), flag_store(bdd_file, BDDFileName), flag_store(bdd_par_file, ParamFileName), problog_kbest(Goal, K, Prob, Status), flag_store(dir, InternWorkingDir), flag_store(bdd_file, InternBDDFlag), flag_store(bdd_par_file, InternParFlag). % ( Status=ok -> % problog_flag(bdd_file,InternBDDFlag), % problog_flag(bdd_par_file,InternParFlag), % convert_filename_to_working_path(InternBDDFlag, InternBDD), % convert_filename_to_working_path(InternParFlag, InternPar), % rename_file(InternBDD,BDDFile), % rename_file(InternPar,ParamFile) % ; % true). problog_kbest(Goal, K, Prob, Status) :- problog_flag(first_threshold,InitT), init_problog_kbest(InitT), problog_control(off,up), problog_kbest_id(Goal, K), retract(current_kbest(_,ListFound,_NumFound)), build_prefixtree(ListFound), nb_getval(problog_completed_proofs, Trie_Completed_Proofs), eval_dnf(Trie_Completed_Proofs,Prob,Status), delete_ptree(Trie_Completed_Proofs). problog_real_kbest(Goal, K, Prob, Status) :- problog_flag(first_threshold,InitT), init_problog_kbest(InitT), problog_control(off,up), problog_kbest_id(Goal, K), retract(current_kbest(_,RawListFound,NumFound)), % limiting the number of proofs is not only needed for fast SLD resolution but also for fast BDD building. % one can't assume that kbest is called for the former and not for the latter take_k_best(RawListFound,K,NumFound,ListFound), build_prefixtree(ListFound), nb_getval(problog_completed_proofs, Trie_Completed_Proofs), eval_dnf(Trie_Completed_Proofs,Prob,Status), delete_ptree(Trie_Completed_Proofs). init_problog_kbest(Threshold) :- retractall(current_kbest(_,_,_)), assertz(current_kbest(-999999,[],0)), %(log-threshold,proofs,num_proofs) init_ptree(Trie_Completed_Proofs), nb_setval(problog_completed_proofs, Trie_Completed_Proofs), 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), open_end_close_end(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 -> assertz(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), assertz(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]) :- !, nb_getval(problog_completed_proofs, Trie_Completed_Proofs), insert_ptree(true,Trie_Completed_Proofs). build_prefixtree([LogP-L|List]) :- ( problog_flag(show_proofs,true) -> get_fact_list(L,ListOfFacts), P is exp(LogP), format(user,'~q ~q~n',[P,ListOfFacts]) ; true ), nb_getval(problog_completed_proofs, Trie_Completed_Proofs), insert_ptree(L,Trie_Completed_Proofs), build_prefixtree(List). take_k_best(In,K,OutOf,Out) :- ( K>=OutOf -> In = Out; In = [_|R], OutOf2 is OutOf-1, take_k_best(R,K,OutOf2,Out) ). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % exact probability % problog_exact(+Goal,-Prob,-Status) % % using all proofs = using all proofs with probability > 0 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% problog_exact(Goal,Prob,Status) :- problog_control(on, exact), problog_low(Goal,0,Prob,Status), problog_control(off, exact). problog_exact_save(Goal,Prob,Status,BDDFile,ParamFile) :- problog_flag(dir, InternWorkingDir), problog_flag(bdd_file, InternBDDFlag), problog_flag(bdd_par_file, InternParFlag), split_path_file(BDDFile, WorkingDir, BDDFileName), split_path_file(ParamFile, _WorkingDir, ParamFileName), flag_store(dir, WorkingDir), flag_store(bdd_file, BDDFileName), flag_store(bdd_par_file, ParamFileName), problog_control(on, exact), problog_low(Goal,0,Prob,Status), problog_control(off, exact), flag_store(dir, InternWorkingDir), flag_store(bdd_file, InternBDDFlag), flag_store(bdd_par_file, InternParFlag). % ( % 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_collect_trie(Goal):- problog_call(Goal), add_solution, fail. problog_collect_trie(_Goal). problog_save_state(State):- nb_getval(problog_completed_proofs, Trie_Completed_Proofs), nb_getval(problog_current_proof, IDs), recordz(problog_stack, store(Trie_Completed_Proofs, IDs), State), init_ptree(Sub_Trie_Completed_Proofs), nb_setval(problog_completed_proofs, Sub_Trie_Completed_Proofs), nb_setval(problog_current_proof, []). problog_restore_state(State):- recorded(problog_stack, store(Trie_Completed_Proofs, IDs), State), erase(State), nb_setval(problog_completed_proofs, Trie_Completed_Proofs), nb_setval(problog_current_proof, IDs). problog_exact_nested(Goal, Prob, Status):- problog_save_state(State), problog_collect_trie(Goal), nb_getval(problog_completed_proofs, Trie_Completed_Proofs), /* writeln(Goal), print_nested_ptree(Trie_Completed_Proofs),*/ eval_dnf(Trie_Completed_Proofs, Prob, Status), delete_ptree(Trie_Completed_Proofs), problog_restore_state(State). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % probability by sampling: % running another N samples until 95percentCI-width 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, statistics(walltime,[T2,_]), Time is (T2-InitialTime),%/1000, problog_convergence_check(Time, Prob, SamplesNew, Delta, _Epsilon, Converged), ((Converged = true; Converged = terminate) -> (problog_flag(verbose,true) -> format('Runtime ~w ms~2n',[Time]) ; true ), assertz(mc_prob(Prob)) ; montecarlo(Goal,Delta,K,SamplesNew,File,Next,InitialTime) ). % Epsilon is 2*sqrt(Prob*(1-Prob)/SamplesNew), % Low is Prob-Epsilon, % High is Prob+Epsilon, % Diff is 2*Epsilon, % (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 % (problog_flag(verbose,true) -> % format('Runtime ~w sec~2n',[Time]) % ; % true % ), % assertz(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), eraseall(mc_true), eraseall(mc_false), reset_non_ground_facts, % problog_abolish_all_tables. problog_tabled(P), problog_abolish_table(P), fail. clean_sample. % find new proof -- need to reset control after init get_some_proof(Goal) :- init_problog(0), problog_control(on,mc), 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),assertz(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. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % find k most likely different answers (using their explanation prob as score) % largely copied+adapted from kbest, uses same dynamic predicate %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% problog_kbest_answers(Goal,K,ResultList) :- problog_flag(first_threshold,InitT), init_problog_kbest(InitT), nb_getval(problog_completed_proofs, Trie_Completed_Proofs), delete_ptree( Trie_Completed_Proofs), % this is just because we reuse init from kbest and don't need the tree problog_control(off,up), problog_kbest_answers_id(Goal, K), retract(current_kbest(_,LogResultList,_NumFound)), transform_loglist_to_result(LogResultList,ResultList). problog_kbest_answers_id(Goal, K) :- problog_call(Goal), copy_term(Goal,GoalCopy), % needed? update_kbest_answers(GoalCopy,K), fail. problog_kbest_answers_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_answers_id(Goal, K)). update_kbest_answers(Goal,K) :- b_getval(problog_probability,NewLogProb), current_kbest(LogThreshold,_,_), (NewLogProb>=LogThreshold -> update_current_kbest_answers(K,NewLogProb,Goal) ; fail). update_current_kbest_answers(_,NewLogProb,Goal) :- current_kbest(_,List,_), update_prob_of_known_answer(List,Goal,NewLogProb,NewList), !, keysort(NewList,SortedList),%format(user_error,'updated variant of ~w~n',[Goal]), retract(current_kbest(K,_,Len)), assertz(current_kbest(K,SortedList,Len)). update_current_kbest_answers(K,NewLogProb,Goal) :- retract(current_kbest(OldThres,List,Length)), sorted_insert(NewLogProb-Goal,List,NewList),%format(user_error,'inserted new element ~w~n',[Goal]), NewLength is Length+1, (NewLength < K -> assertz(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), assertz(current_kbest(NewThres,FinalList,FinalLength))). % this fails if there is no variant -> go to second case above update_prob_of_known_answer([OldLogP-OldGoal|List],Goal,NewLogProb,[MaxLogP-OldGoal|List]) :- variant(OldGoal,Goal), !, MaxLogP is max(OldLogP,NewLogProb). update_prob_of_known_answer([First|List],Goal,NewLogProb,[First|NewList]) :- update_prob_of_known_answer(List,Goal,NewLogProb,NewList). transform_loglist_to_result(In,Out) :- transform_loglist_to_result(In,[],Out). transform_loglist_to_result([],Acc,Acc). transform_loglist_to_result([LogP-G|List],Acc,Result) :- P is exp(LogP), transform_loglist_to_result(List,[P-G|Acc],Result). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % GENERAL PURPOSE PREDICATES FOR DTPROBLOG %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do inference of a single goal, using the default inference method %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% problog_infer(Goal,Prob) :- problog_flag(inference,Method), problog_infer(Method,Goal,Prob). problog_infer(exact,Goal,Prob) :- problog_exact(Goal,Prob,ok). problog_infer(atleast-K-best,Goal,Prob) :- problog_kbest(Goal,K,Prob,ok). problog_infer(K-best,Goal,Prob) :- problog_real_kbest(Goal,K,Prob,ok). problog_infer(montecarlo(Confidence),Goal,Prob) :- problog_montecarlo(Goal,Confidence,Prob). problog_infer(delta(Width),Goal,Prob) :- problog_delta(Goal,Width,Bound_low,Bound_up,ok), Prob is 0.5*(Bound_low+Bound_up). problog_infer(low(Threshold),Goal,Prob) :- problog_low(Goal,Threshold,Prob,ok). problog_infer(threshold(Threshold),Goal,Prob) :- problog_threshold(Goal,Threshold,Bound_low,Bound_up,ok), Prob is 0.5*(Bound_low+Bound_up). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do inference of a set of queries, using the default inference method %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% problog_infer_forest([],[]) :- !. problog_infer_forest(Goals,Probs) :- (problog_infer_forest_supported -> problog_bdd_forest(Goals), length(Goals,N), eval_bdd_forest(N,Probs,ok) ; throw(error('Flag settings not supported by problog_infer_forest/1.')) ). problog_infer_forest_supported :- problog_bdd_forest_supported. eval_bdd_forest(N,Probs,Status) :- bdd_files(BDDFile,BDDParFile), problog_flag(bdd_time,BDDTime), (problog_flag(dynamic_reorder, true) -> ParamD = '' ; ParamD = ' -dreorder' ), (problog_flag(bdd_static_order, true) -> problog_flag(static_order_file, FileName), convert_filename_to_working_path(FileName, SOFileName), atomic_concat([ParamD, ' -sord ', SOFileName], Param) ; Param = ParamD ), convert_filename_to_problog_path('problogbdd', ProblogBDD), problog_flag(bdd_result,ResultFileFlag), convert_filename_to_working_path(ResultFileFlag, ResultFile), atomic_concat([ProblogBDD, Param,' -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_probs(N,Probs), seen, Status = ok, % cleanup % TODO handle flag for keeping files (problog_flag(save_bdd,true) -> true ; delete_file(BDDFile), delete_file(BDDParFile), delete_file(ResultFile), delete_bdd_forest_files(N) ) ). read_probs(N,Probs) :- (N = 0 -> Probs = [] ; Probs = [Prob|Rest], read(probability(Prob)), N2 is N-1, read_probs(N2,Rest) ). delete_bdd_forest_files(N) :- (N=0 -> true ; bdd_forest_file(N,BDDFile), delete_file(BDDFile,[]), N2 is N-1, delete_bdd_forest_files(N2) ). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Build a trie using the default inference method %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% build_trie(Goal, Trie) :- (build_trie_supported -> problog_flag(inference,Method), once(build_trie(Method, Goal, Trie)) ; throw(error('Flag settings not supported by build_trie/2.')) ). build_trie_supported :- problog_flag(inference,exact). build_trie_supported :- problog_flag(inference,low(_)). build_trie_supported :- problog_flag(inference,atleast-_-best). build_trie_supported :- problog_flag(inference,_-best). build_trie(exact, Goal, Trie) :- problog_control(on, exact), build_trie(low(0), Goal, Trie), problog_control(off, exact). build_trie(low(Threshold), Goal, _) :- number(Threshold), init_problog_low(Threshold), problog_control(off, up), statistics(walltime, _), problog_call(Goal), add_solution, fail. build_trie(low(Threshold), _, Trie) :- number(Threshold), statistics(walltime, [_,E]), problog_var_set(sld_time, E), nb_getval(problog_completed_proofs, Trie). % don't clear tabling; tables can be reused by other query build_trie(atleast-K-best, Goal, Trie) :- number(K), problog_flag(first_threshold,InitT), init_problog_kbest(InitT), problog_control(off,up), problog_kbest_id(Goal, K), retract(current_kbest(_,ListFound,_NumFound)), build_prefixtree(ListFound), nb_getval(problog_completed_proofs, Trie), clear_tabling. % clear tabling because tables cannot be reused by other query build_trie(K-best, Goal, Trie) :- number(K), problog_flag(first_threshold,InitT), init_problog_kbest(InitT), problog_control(off,up), problog_kbest_id(Goal, K), retract(current_kbest(_,RawListFound,NumFound)), % limiting the number of proofs is not only needed for fast SLD resolution but also for fast BDD building. % one can't assume that kbest is called for the former and not for the latter % thus, we take EXACTLY k proofs take_k_best(RawListFound,K,NumFound,ListFound), build_prefixtree(ListFound), nb_getval(problog_completed_proofs, Trie), clear_tabling. % clear tabling because tables cannot be reused by other query %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Write BDD structure script for a trie and list all variables used %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% write_bdd_struct_script(Trie,BDDFile,Variables) :- % Check whether we use Hybrid ProbLog ( hybrid_proof(_,_,_) -> ( % Yes! run the disjoining stuff retractall(hybrid_proof_disjoint(_,_,_,_)), disjoin_hybrid_proofs, init_ptree(OriTrie), % use this as tmp ptree %%%%%%%%%%%%%%%%%%%%% ( % go over all stored proofs enum_member_ptree(List,OriTrie1), ( List=[_|_] -> Proof=List; Proof=[List] ), ( select(continuous(ProofID),Proof,Rest) -> ( % this proof is using continuous facts all_hybrid_subproofs(ProofID,List2), append(Rest,List2,NewProof), insert_ptree(NewProof,OriTrie) ); insert_ptree(Proof,OriTrie) ), fail; true ) %%%%%%%%%%%%%%%%%%%%% ) ; % Nope, just pass on the Trie OriTrie=OriTrie1 ), ((problog_flag(variable_elimination, true), nb_getval(problog_nested_tries, false)) -> statistics(walltime, _), trie_check_for_and_cluster(OriTrie), statistics(walltime, [_, VariableEliminationTime]), trie_replace_and_cluster(OriTrie, Trie), problog_var_set(variable_elimination_time, VariableEliminationTime), variable_elimination_stats(Clusters, OrigPF, CompPF), problog_var_set(variable_elimination_stats, compress(Clusters, OrigPF, CompPF)), clean_up ; Trie = OriTrie ), (problog_flag(bdd_static_order, true) -> get_order(Trie, Order), problog_flag(static_order_file, SOFName), convert_filename_to_working_path(SOFName, SOFileName), generate_order_by_prob_fact_appearance(Order, SOFileName) ; true ), ptree:trie_stats(Memory, Tries, Entries, Nodes), (nb_getval(problog_nested_tries, false) -> ptree:trie_usage(Trie, TEntries, TNodes, TVirtualNodes), problog_var_set(trie_statistics, tries(memory(Memory), tries(Tries), entries(TEntries), nodes(TNodes), virtualnodes(TVirtualNodes))) ; problog_var_set(trie_statistics, tries(memory(Memory), tries(Tries), entries(Entries), nodes(Nodes))) ), (problog_flag(triedump, true) -> convert_filename_to_working_path(trie_file, TrieFile), tell(TrieFile), print_nested_ptree(Trie), flush_output, told, tell(user_output) ; true ), nb_getval(problog_completed_proofs, Trie_Completed_Proofs), ((Trie = Trie_Completed_Proofs, problog_flag(save_bdd, true)) -> problog_control(on, remember) ; problog_control(off, remember) ), % old reduction method doesn't support nested tries ((problog_flag(use_old_trie, true), nb_getval(problog_nested_tries, false)) -> statistics(walltime, _), (problog_control(check, remember) -> bdd_struct_ptree_map(Trie, BDDFile, Variables, Mapping), convert_filename_to_working_path(save_map, MapFile), tell(MapFile), format('mapping(~q).~n', [Mapping]), flush_output, told ; bdd_struct_ptree(Trie, BDDFile, Variables) ), statistics(walltime, [_, ScriptGenerationTime]), problog_var_set(bdd_script_time, ScriptGenerationTime) % omitted call to execute_bdd_tool ; true ), % naive method with nested trie support but not loops ((problog_flag(use_naive_trie, true); (problog_flag(use_old_trie, true), nb_getval(problog_nested_tries, true))) -> statistics(walltime, _), atomic_concat([BDDFile, '_naive'], BDDFile_naive), nested_ptree_to_BDD_struct_script(Trie, BDDFile_naive, Variables), statistics(walltime, [_, ScriptGenerationTime_naive]), problog_var_set(bdd_script_time(naive), ScriptGenerationTime_naive) % omitted call to execute_bdd_tool ; true ), % reduction method with depth_breadth trie support problog_flag(db_trie_opt_lvl, ROptLevel), problog_flag(db_min_prefix, MinPrefix), (problog_flag(compare_opt_lvl, true) -> generate_ints(0, ROptLevel, Levels) ; Levels = [ROptLevel] ), % Removed forall here, because it hides 'Variables' from what comes afterwards memberchk(OptLevel, Levels), ( (problog_flag(use_db_trie, true) -> tries:trie_db_opt_min_prefix(MinPrefix), statistics(walltime, _), (nb_getval(problog_nested_tries, false) -> trie_to_bdd_struct_trie(Trie, DBTrie, BDDFile, OptLevel, Variables) ; nested_trie_to_bdd_struct_trie(Trie, DBTrie, BDDFile, OptLevel, Variables) ), atomic_concat(['builtin_', OptLevel], Builtin), ptree:trie_stats(DBMemory, DBTries, DBEntries, DBNodes), FM is DBMemory - Memory, FT is DBTries - Tries, FE is DBEntries - Entries, FN is DBNodes - Nodes, problog_var_set(dbtrie_statistics(Builtin), tries(memory(FM), tries(FT), entries(FE), nodes(FN))), delete_ptree(DBTrie), statistics(walltime, [_, ScriptGenerationTime_builtin]), problog_var_set(bdd_script_time(Builtin), ScriptGenerationTime_builtin) % omitted call to execute_bdd_tool ; true ) ), % decomposition method (problog_flag(use_dec_trie, true) -> statistics(walltime, _), atomic_concat([BDDFile, '_dec'], BDDFile_dec), ptree_decomposition_struct(Trie, BDDFile_dec, Variables), statistics(walltime, [_, ScriptGenerationTime_dec]), problog_var_set(bdd_script_time(dec), ScriptGenerationTime_dec) % omitted call to execute_bdd_tool ; true ), (Trie =\= OriTrie -> delete_ptree(Trie) ; true ), (var(Variables) -> throw(error('novars')) ; true). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Building a forest of BDDs %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% problog_bdd_forest(Goals) :- (problog_bdd_forest_supported -> require(keep_ground_ids), once(write_bdd_forest(Goals,[],Vars,1)), unrequire(keep_ground_ids), reset_non_ground_facts, bdd_par_file(BDDParFile), tell(BDDParFile), bdd_vars_script(Vars), flush_output, % isnt this called by told/0? told, length(Goals,L), length(Vars,NbVars), write_global_bdd_file(NbVars,L), (problog_flag(retain_tables, true) -> retain_tabling; true), clear_tabling ; throw(error('Flag settings not supported by problog_bdd_forest/1.')) ). problog_bdd_forest_supported :- build_trie_supported. % Iterate over all Goals, write BDD scripts and collect variables used. write_bdd_forest([],VarsTot,VarsTot,_). write_bdd_forest([Goal|Rest],VarsAcc,VarsTot,N):- build_trie(Goal, Trie), write_nth_bdd_struct_script(N, Trie, Vars), (problog_flag(verbose, true)-> problog_statistics ; true ), delete_ptree(Trie), N2 is N+1, list_to_ord_set(Vars,VarsSet), ord_union(VarsAcc,VarsSet,VarsAcc2), once(write_bdd_forest(Rest,VarsAcc2,VarsTot,N2)). % Write files write_nth_bdd_struct_script(N,Trie,Vars) :- bdd_forest_file(N,BDDFile), write_bdd_struct_script(Trie,BDDFile,Vars). write_global_bdd_file(NbVars,L) :- bdd_file(BDDFile), open(BDDFile,'write',BDDFileStream), tell(BDDFileStream), format('@BDD2~n~w~n~w~n~w~n',[NbVars,0,L]), write_global_bdd_file_line(1,L), write_global_bdd_file_query(1,L), flush_output, told. write_global_bdd_file_line(I,Max) :- (I>Max -> true ; bdd_forest_file(I,BDDFile), format("L~q = <~w>~n",[I,BDDFile]), I2 is I+1, write_global_bdd_file_line(I2,Max) ). write_global_bdd_file_query(I,Max) :- (I=Max -> format("L~q~n",[I]) ; format("L~q,",[I]), I2 is I+1, write_global_bdd_file_query(I2,Max) ). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Filename specifications %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% bdd_forest_file(N,BDDFile) :- problog_flag(bdd_file,BDDFileFlag), atomic_concat([BDDFileFlag,'_',N],BDDFileFlagWithN), convert_filename_to_working_path(BDDFileFlagWithN, BDDFile). bdd_files(BDDFile,BDDParFile) :- bdd_file(BDDFile), bdd_par_file(BDDParFile). bdd_file(BDDFile) :- problog_flag(bdd_file, BDDFileFlag), convert_filename_to_working_path(BDDFileFlag, BDDFile). bdd_par_file(BDDParFile) :- problog_flag(bdd_par_file, BDDParFileFlag), convert_filename_to_working_path(BDDParFileFlag, BDDParFile). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Persistent Ground IDs %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% require(Feature) :- atom(Feature), atomic_concat(['problog_required_',Feature],Feature_Required), atomic_concat([Feature_Required,'_',depth],Feature_Depth), (required(Feature) -> b_getval(Feature_Depth,Depth), Depth1 is Depth+1, b_setval(Feature_Depth,Depth1) ; b_setval(Feature_Required,required), b_setval(Feature_Depth,1) %,format("starting to require ~q~n",[Feature]) ). unrequire(Feature) :- atom(Feature), atomic_concat(['problog_required_',Feature],Feature_Required), atomic_concat([Feature_Required,'_',depth],Feature_Depth), b_getval(Feature_Depth,Depth), (Depth=1 -> nb_delete(Feature_Required), nb_delete(Feature_Depth) %,format("stopped keeping ground id's~n",[]) ; Depth1 is Depth-1, b_setval(Feature_Depth,Depth1) ). required(Feature) :- atom(Feature), atomic_concat(['problog_required_',Feature],Feature_Required), catch(b_getval(Feature_Required,Val),error(existence_error(variable,Feature_Required),_),fail), Val == required. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Should go to dtproblog.yap %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% signal_decision(ClauseID,GroundID) :- (decision_fact(ClauseID,_) -> bb_get(decisions,S), ord_insert(S, GroundID, S2), bb_put(decisions,S2) ; true ). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Term Expansion for user predicates % Must come after clauses for '::'/2 and term_expansion_intern/3 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% user:term_expansion(Term,ExpandedTerm) :- Term \== end_of_file, prolog_load_context(module,Mod), problog:term_expansion_intern(Term,Mod,ExpandedTerm).