From ff6bd1dda0304878fd8bde71e714dacd147c9735 Mon Sep 17 00:00:00 2001 From: Theofrastos Mantadelis Date: Tue, 5 Oct 2010 18:29:29 +0200 Subject: [PATCH] ProbLog Versioning System --- packages/ProbLog/problog_examples/problog.yap | 3133 ----------------- .../problog_examples/problog_learning.yap | 1677 --------- 2 files changed, 4810 deletions(-) delete mode 100644 packages/ProbLog/problog_examples/problog.yap delete mode 100644 packages/ProbLog/problog_examples/problog_learning.yap diff --git a/packages/ProbLog/problog_examples/problog.yap b/packages/ProbLog/problog_examples/problog.yap deleted file mode 100644 index 873c53837..000000000 --- a/packages/ProbLog/problog_examples/problog.yap +++ /dev/null @@ -1,3133 +0,0 @@ -%%% -*- Mode: Prolog; -*- - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -% -% $Date: 2010-10-05 18:15:57 +0200 (Tue, 05 Oct 2010) $ -% $Revision: 4876 $ -% -% 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. 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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, - 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). - diff --git a/packages/ProbLog/problog_examples/problog_learning.yap b/packages/ProbLog/problog_examples/problog_learning.yap deleted file mode 100644 index b8c2ff1f0..000000000 --- a/packages/ProbLog/problog_examples/problog_learning.yap +++ /dev/null @@ -1,1677 +0,0 @@ -%%% -*- Mode: Prolog; -*- - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -% -% $Date: 2010-10-05 16:52:13 +0200 (Tue, 05 Oct 2010) $ -% $Revision: 4869 $ -% -% 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: -% Bernd Gutmann -% -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -% -% 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. 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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. -% -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - - -:- module(learning,[do_learning/1, - do_learning/2, - reset_learning/0 - ]). - -% switch on all the checks to reduce bug searching time -:- style_check(all). -:- yap_flag(unknown,error). - -% load modules from the YAP library -:- use_module(library(lists), [max_list/2, min_list/2, sum_list/2]). -:- use_module(library(system), [delete_file/1, file_exists/1, shell/2]). - -% load our own modules -:- use_module(problog). -:- use_module('problog/logger'). -:- use_module('problog/flags'). -:- use_module('problog/os'). -:- use_module('problog/print_learning'). -:- use_module('problog/utils_learning'). - -% used to indicate the state of the system -:- dynamic(values_correct/0). -:- dynamic(learning_initialized/0). -:- dynamic(current_iteration/1). -:- dynamic(example_count/1). -:- dynamic(query_probability_intern/2). -:- dynamic(query_gradient_intern/4). -:- dynamic(last_mse/1). -:- dynamic(query_is_similar/2). -:- dynamic(query_md5/2). - - -% used to identify queries which have identical proofs -:- dynamic(query_is_similar/2). -:- dynamic(query_md5/3). - -:- multifile(user:example/4). -user:example(A,B,C,=) :- - current_predicate(user:example/3), - user:example(A,B,C). - -:- multifile(user:test_example/4). -user:test_example(A,B,C,=) :- - current_predicate(user:test_example/3), - user:test_example(A,B,C). - - -%======================================================================== -%= store the facts with the learned probabilities to a file -%= if F is a variable, a filename based on the current iteration is used -%= -%======================================================================== - -save_model:- - current_iteration(Iteration), - atomic_concat(['factprobs_',Iteration,'.pl'],Filename), - problog_flag(output_directory,Dir), - concat_path_with_filename(Dir,Filename,Filename2), - export_facts(Filename2). - - -%======================================================================== -%= store the current succes probabilities for training and test examples -%= -%======================================================================== - -save_predictions:- - current_iteration(Iteration), - atomic_concat(['predictions_',Iteration,'.pl'],Filename), - problog_flag(output_directory,Dir), - concat_path_with_filename(Dir,Filename,Filename2), - - open(Filename2,'append',Handle), - format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",[]), - format(Handle,"% Iteration, train/test, QueryID, Query, GroundTruth, Prediction %\n",[]), - format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",[]), - !, - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % start save prediction test examples - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - ( % go over all test examples - current_predicate(user:test_example/4), - user:test_example(Query_ID,Query,TrueQueryProb,_), - query_probability(Query_ID,LearnedQueryProb), - - format(Handle,'ex(~q,test,~q,~q,~10f,~10f).\n', - [Iteration,Query_ID,Query,TrueQueryProb,LearnedQueryProb]), - - fail; % go to next test example - true - ), - !, - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % stop save prediction test examples - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % start save prediction training examples - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - ( % go over all training examples - current_predicate(user:example/4), - user:example(Query_ID,Query,TrueQueryProb,_), - query_probability(Query_ID,LearnedQueryProb), - - format(Handle,'ex(~q,train,~q,~q,~10f,~10f).\n', - [Iteration,Query_ID,Query,TrueQueryProb,LearnedQueryProb]), - - fail; % go to next training example - true - ), - !, - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % stop save prediction training examples - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - - format(Handle,'~3n',[]), - close(Handle). - - - -%======================================================================== -%= find out whether some example IDs are used more than once -%= if so, complain and stop -%= -%======================================================================== - -check_examples :- - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % Check example IDs - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - ( - (current_predicate(user:example/4),user:example(ID,_,_,_), \+ atomic(ID)) - -> - ( - format(user_error,'The example id of training example ~q ',[ID]), - format(user_error,'is not atomic (e.g foo42, 23, bar, ...).~n',[]), - throw(error(examples)) - ); true - ), - - ( - (current_predicate(user:test_example/4),user:test_example(ID,_,_,_), \+ atomic(ID)) - -> - ( - format(user_error,'The example id of test example ~q ',[ID]), - format(user_error,'is not atomic (e.g foo42, 23, bar, ...).~n',[]), - throw(error(examples)) - ); true - ), - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % Check example probabilities - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - ( - (current_predicate(user:example/4),user:example(ID,_,P,_), (\+ number(P); P>1 ; P<0)) - -> - ( - format(user_error,'The training example ~q does not have a valid probability value (~q).~n',[ID,P]), - throw(error(examples)) - ); true - ), - - ( - (current_predicate(user:test_example/4),user:test_example(ID,_,P,_), (\+ number(P); P>1 ; P<0)) - -> - ( - format(user_error,'The test example ~q does not have a valid probability value (~q).~n',[ID,P]), - throw(error(examples)) - ); true - ), - - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % Check that no example ID is repeated, - % and if it is repeated make sure the query is the same - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - ( - ( - ( - current_predicate(user:example/4), - user:example(ID,QueryA,_,_), - user:example(ID,QueryB,_,_), - QueryA \= QueryB - ) ; - - ( - current_predicate(user:test_example/4), - user:test_example(ID,QueryA,_,_), - user:test_example(ID,QueryB,_,_), - QueryA \= QueryB - ); - - ( - current_predicate(user:example/4), - current_predicate(user:test_example/4), - user:example(ID,QueryA,_,_), - user:test_example(ID,QueryB,_,_), - QueryA \= QueryB - ) - ) - -> - ( - format(user_error,'The example id ~q is used several times.~n',[ID]), - throw(error(examples)) - ); true - ). -%======================================================================== -%= -%======================================================================== - -reset_learning :- - retractall(current_iteration(_)), - retractall(learning_initialized), - - retractall(values_correct), - retractall(current_iteration(_)), - retractall(example_count(_)), - retractall(query_probability_intern(_,_)), - retractall(query_gradient_intern(_,_,_)), - retractall(last_mse(_)), - retractall(query_is_similar(_,_)), - retractall(query_md5(_,_,_)), - - set_problog_flag(alpha,auto), - set_problog_flag(learning_rate,examples), - logger_reset_all_variables. - - - -%======================================================================== -%= initialize everything and perform Iterations times gradient descent -%= can be called several times -%= if it is called with an epsilon parameter, it stops when the change -%= in the MSE is smaller than epsilon -%======================================================================== - -do_learning(Iterations) :- - do_learning(Iterations,-1). - -do_learning(Iterations,Epsilon) :- - current_predicate(user:example/4), - !, - integer(Iterations), - number(Epsilon), - Iterations>0, - do_learning_intern(Iterations,Epsilon). -do_learning(_,_) :- - format(user_error,'~n~Error: No training examples specified.~n~n',[]). - - -do_learning_intern(0,_) :- - !. -do_learning_intern(Iterations,Epsilon) :- - Iterations>0, - - init_learning, - current_iteration(CurrentIteration), - retractall(current_iteration(_)), - NextIteration is CurrentIteration+1, - assertz(current_iteration(NextIteration)), - EndIteration is CurrentIteration+Iterations-1, - - format_learning(1,'~nIteration ~d of ~d~n',[CurrentIteration,EndIteration]), - logger_set_variable(iteration,CurrentIteration), - - logger_start_timer(duration), - - mse_testset, - ground_truth_difference, - gradient_descent, - - problog_flag(log_frequency,Log_Frequency), - - ( - ( Log_Frequency>0, 0 =:= CurrentIteration mod Log_Frequency) - -> - ( - once(save_predictions), - once(save_model) - ); - true - ), - - update_values, - - ( - last_mse(Last_MSE) - -> - ( - retractall(last_mse(_)), - logger_get_variable(mse_trainingset,Current_MSE), - assertz(last_mse(Current_MSE)), - !, - MSE_Diff is abs(Last_MSE-Current_MSE) - ); ( - logger_get_variable(mse_trainingset,Current_MSE), - assertz(last_mse(Current_MSE)), - MSE_Diff is Epsilon+1 - ) - ), - - ( - (problog_flag(rebuild_bdds,BDDFreq),BDDFreq>0,0 =:= CurrentIteration mod BDDFreq) - -> - ( - retractall(values_correct), - retractall(query_is_similar(_,_)), - retractall(query_md5(_,_,_)), - empty_bdd_directory, - init_queries - ); true - ), - - - !, - logger_stop_timer(duration), - - - logger_write_data, - - - - RemainingIterations is Iterations-1, - - ( - MSE_Diff>Epsilon - -> - do_learning_intern(RemainingIterations,Epsilon); - true - ). - - -%======================================================================== -%= find proofs and build bdds for all training and test examples -%= -%= -%======================================================================== -init_learning :- - learning_initialized, - !. -init_learning :- - check_examples, - - logger_write_header, - - format_learning(1,'Initializing everything~n',[]), - empty_output_directory, - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % Delete the BDDs from the previous run if they should - % not be reused - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - ( - ( - problog_flag(reuse_initialized_bdds,true), - problog_flag(rebuild_bdds,0) - ) - -> - true; - empty_bdd_directory - ), - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % Check, if continuous facts are used. - % if yes, switch to problog_exact - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - problog_flag(init_method,(_,_,_,_,OldCall)), - ( - ( - continuous_fact(_), - OldCall\=problog_exact_save(_,_,_,_,_) - ) - -> - ( - format('Theory uses continuous facts.~nWill use problog_exact/3 as initalization method.~2n',[]), - set_problog_flag(init_method,(Query,Probability,BDDFile,ProbFile,problog_exact_save(Query,Probability,_Status,BDDFile,ProbFile))) - ); - true - ), - - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % start count test examples - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - bb_put(test_examples,0), - ( % go over all test examples - current_predicate(user:test_example/4), - user:test_example(_,_,_,_), - bb_get(test_examples, OldCounter), - NewCounter is OldCounter+1, - bb_put(test_examples,NewCounter), - - fail; % go to next text example - true - ), - bb_delete(test_examples,TestExampleCount), - format_learning(3,'~q test examples~n',[TestExampleCount]), - !, - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % stop count test examples - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % start count training examples - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - bb_put(training_examples,0), - ( % go over all training examples - current_predicate(user:example/4), - user:example(_,_,_,_), - bb_get(training_examples, OldCounter), - NewCounter is OldCounter+1, - bb_put(training_examples,NewCounter), - - fail; %go to next training example - true - ), - bb_delete(training_examples,TrainingExampleCount), - assertz(example_count(TrainingExampleCount)), - format_learning(3,'~q training examples~n',[TrainingExampleCount]), - !, - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % stop count training examples - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % set learning rate and alpha - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - ( - problog_flag(learning_rate,examples) - -> - set_problog_flag(learning_rate,TrainingExampleCount); - true - ), - - ( - problog_flag(alpha,auto) - -> - auto_alpha; - true - ), - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % build BDD script for every example - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - once(init_queries), - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % done - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - assertz(current_iteration(0)), - assertz(learning_initialized), - - format_learning(1,'~n',[]). - - - -%======================================================================== -%= This predicate goes over all training and test examples, -%= calls the inference method of ProbLog and stores the resulting -%= BDDs -%======================================================================== - - -init_queries :- - format_learning(2,'Build BDDs for examples~n',[]), - ( % go over all test examples - current_predicate(user:test_example/4), - user:test_example(ID,Query,Prob,_), - format_learning(3,' test example ~q: ~q~n',[ID,Query]), - flush_output(user), - init_one_query(ID,Query,test), - - fail; % go to next test example - true - ), - ( % go over all training examples - current_predicate(user:example/4), - user:example(ID,Query,Prob,_), - format_learning(3,' training example ~q: ~q~n',[ID,Query]), - flush_output(user), - init_one_query(ID,Query,training), - - fail; %go to next training example - true - ). - - -bdd_input_file(Filename) :- - problog_flag(output_directory,Dir), - concat_path_with_filename(Dir,'input.txt',Filename). - -init_one_query(QueryID,Query,Type) :- - bdd_input_file(Probabilities_File), - problog_flag(bdd_directory,Query_Directory), - - atomic_concat(['query_',QueryID],Filename1), - concat_path_with_filename(Query_Directory,Filename1,Filename), - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % if BDD file does not exist, call ProbLog - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - ( - file_exists(Filename) - -> - format_learning(3,' Reuse existing BDD ~q~n~n',[Filename]); - ( - problog_flag(init_method,(Query,_Prob,Filename,Probabilities_File,Call)), - once(Call), - delete_file(Probabilities_File) - ) - ), - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % check wether this BDD is similar to another BDD - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - ( - problog_flag(check_duplicate_bdds,true) - -> - ( - calc_md5(Filename,Query_MD5), - ( - query_md5(OtherQueryID,Query_MD5,Type) - -> - ( - assertz(query_is_similar(QueryID,OtherQueryID)), - format_learning(3, '~q is similar to ~q~2n', [QueryID,OtherQueryID]) - ); - assertz(query_md5(QueryID,Query_MD5,Type)) - ) - ); - - true - ),!, - garbage_collect. - - - - -%======================================================================== -%= updates all values of query_probability/2 and query_gradient/4 -%= should be called always before these predicates are accessed -%= if the old values are still valid, nothing happens -%======================================================================== - -update_values :- - values_correct, - !. -update_values :- - \+ values_correct, - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % delete old values - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - retractall(query_probability_intern(_,_)), - retractall(query_gradient_intern(_,_,_,_)), - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % start write current probabilities to file - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - bdd_input_file(Probabilities_File), - delete_file_silent(Probabilities_File), - - open(Probabilities_File,'write',Handle), - - ( % go over all probabilistic facts - get_fact_probability(ID,Prob), - inv_sigmoid(Prob,Value), - ( - non_ground_fact(ID) - -> - format(Handle,'@x~q_*~n~10f~n',[ID,Value]); - format(Handle,'@x~q~n~10f~n',[ID,Value]) - ), - - fail; % go to next probabilistic fact - true - ), - - ( % go over all continuous facts - get_continuous_fact_parameters(ID,gaussian(Mu,Sigma)), - format(Handle,'@x~q_*~n0~n0~n~10f;~10f~n',[ID,Mu,Sigma]), - - fail; % go to next continuous fact - true - ), - - close(Handle), - !, - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % stop write current probabilities to file - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - - assertz(values_correct). - - - -%======================================================================== -%= -%= -%= -%======================================================================== - -update_query_cleanup(QueryID) :- - ( - (query_is_similar(QueryID,_) ; query_is_similar(_,QueryID)) - -> - % either this query is similar to another or vice versa, - % therefore we don't delete anything - true; - retractall(query_gradient_intern(QueryID,_,_,_)) - ). - - -update_query(QueryID,Symbol,What_To_Update) :- - % fixme OS trouble - problog_flag(output_directory,Output_Directory), - problog_flag(bdd_directory,Query_Directory), - bdd_input_file(Probabilities_File), - ( - query_is_similar(QueryID,_) - -> - % we don't have to evaluate the BDD - format_learning(4,'#',[]); - ( - problog_flag(sigmoid_slope,Slope), - problog_dir(PD), - ((What_To_Update=all;query_is_similar(_,QueryID)) -> Method='g' ; Method='l'), - atomic_concat([PD, - '/problogbdd', - ' -i "', Probabilities_File, '"', - ' -l "', Query_Directory,'/query_',QueryID, '"', - ' -m ', Method, - ' -id ', QueryID, - ' -sl ', Slope, - ' > "', - Output_Directory, - 'values.pl"'],Command), - shell(Command,Error), - - - ( - Error = 2 - -> - throw(error('SimpleCUDD has been interrupted.')); - true - ), - ( - Error \= 0 - -> - ( - format(user_error,'SimpleCUDD stopped with error code ~q, command was ~q~n',[Error, shell(Command,Error)]), - throw(bdd_error(QueryID,Error))); - true - ), - atomic_concat([Output_Directory,'values.pl'],Values_Filename), - ( - file_exists(Values_Filename) - -> - ( - ( - once(my_load(Values_Filename,QueryID)) - -> - true; - ( - format(user_error,'ERROR: Tried to read the file ~q but my_load/1 fails.~n~q.~2n',[Values_Filename,update_query(QueryID,Symbol,What_To_Update)]), - throw(error(my_load_fails)) - ) - ); - ( - format(user_error,'ERROR: Tried to read the file ~q but it does not exist.~n~q.~2n',[Values_Filename,update_query(QueryID,Symbol,What_To_Update)]), - throw(error(output_file_does_not_exist)) - ) - ) - ), - - delete_file(Values_Filename), - format_learning(4,'~w',[Symbol]) - ) - ), - flush_output(user). - - -%======================================================================== -%= This predicate reads probability and gradient values from the file -%= the gradient ID is a mere check to uncover hidden bugs -%= +Filename +QueryID -QueryProbability -%======================================================================== - -my_load(File,QueryID) :- - open(File,'read',Handle), - read(Handle,Atom), - once(my_load_intern(Atom,Handle,QueryID)), - close(Handle). -my_load(File,QueryID) :- - format(user_error,'Error at ~q.~2n',[my_load(File,QueryID)]), - throw(error(my_load(File,QueryID))). - -my_load_intern(end_of_file,_,_) :- - !. -my_load_intern(query_probability(QueryID,Prob),Handle,QueryID) :- - !, - assertz(query_probability_intern(QueryID,Prob)), - read(Handle,X), - my_load_intern(X,Handle,QueryID). -my_load_intern(query_gradient(QueryID,XFactID,Type,Value),Handle,QueryID) :- - !, - atomic_concat(x,StringFactID,XFactID), - atom_number(StringFactID,FactID), - assertz(query_gradient_intern(QueryID,FactID,Type,Value)), - read(Handle,X), - my_load_intern(X,Handle,QueryID). -my_load_intern(X,Handle,QueryID) :- - format(user_error,'Unknown atom ~q in results file.~n',[X]), - read(Handle,X2), - my_load_intern(X2,Handle,QueryID). - - - - -%======================================================================== -%= -%= -%= -%======================================================================== -query_probability(QueryID,Prob) :- - ( - query_probability_intern(QueryID,Prob) - -> - true; - ( - query_is_similar(QueryID,OtherQueryID), - query_probability_intern(OtherQueryID,Prob) - ) - ). -query_gradient(QueryID,Fact,Type,Value) :- - ( - query_gradient_intern(QueryID,Fact,Type,Value) - -> - true; - ( - query_is_similar(QueryID,OtherQueryID), - query_gradient_intern(OtherQueryID,Fact,Type,Value) - ) - ). - -%======================================================================== -%= -%= -%= -%======================================================================== - - - -% FIXME -ground_truth_difference :- - findall(Diff,(tunable_fact(FactID,GroundTruth), - \+continuous_fact(FactID), - \+ var(GroundTruth), - get_fact_probability(FactID,Prob), - Diff is abs(GroundTruth-Prob)),AllDiffs), - ( - AllDiffs=[] - -> - ( - MinDiff=0.0, - MaxDiff=0.0, - DiffMean=0.0 - ) ; - ( - length(AllDiffs,Len), - sum_list(AllDiffs,AllDiffsSum), - min_list(AllDiffs,MinDiff), - max_list(AllDiffs,MaxDiff), - DiffMean is AllDiffsSum/Len - ) - ), - - logger_set_variable(ground_truth_diff,DiffMean), - logger_set_variable(ground_truth_mindiff,MinDiff), - logger_set_variable(ground_truth_maxdiff,MaxDiff). - -%======================================================================== -%= Calculates the mse of training and test data -%= -%= -Float -%======================================================================== - -mse_trainingset_only_for_linesearch(MSE) :- - ( - current_predicate(user:example/4) - -> - ( - update_values, - findall(SquaredError, - (user:example(QueryID,_Query,QueryProb,Type), - once(update_query(QueryID,'.',probability)), - query_probability(QueryID,CurrentProb), - once(update_query_cleanup(QueryID)), - ( - (Type == '='; (Type == '<', CurrentProb>QueryProb); (Type=='>',CurrentProb - SquaredError is (CurrentProb-QueryProb)**2; - SquaredError = 0.0 - ) - ), - - AllSquaredErrors), - - length(AllSquaredErrors,Length), - sum_list(AllSquaredErrors,SumAllSquaredErrors), - MSE is SumAllSquaredErrors/Length, - format_learning(3,' (~8f)~n',[MSE]) - ); true - ), - retractall(values_correct). - -mse_testset :- - ( - (current_predicate(user:test_example/4),user:test_example(_,_,_,_)) - -> - ( - format_learning(2,'MSE_Test ',[]), - update_values, - findall(SquaredError, - (user:test_example(QueryID,_Query,QueryProb,Type), - once(update_query(QueryID,'+',probability)), - query_probability(QueryID,CurrentProb), - once(update_query_cleanup(QueryID)), - ( - (Type == '='; (Type == '<', CurrentProb>QueryProb); (Type=='>',CurrentProb - SquaredError is (CurrentProb-QueryProb)**2; - SquaredError = 0.0 - ) - ), - AllSquaredErrors), - - length(AllSquaredErrors,Length), - sum_list(AllSquaredErrors,SumAllSquaredErrors), - min_list(AllSquaredErrors,MinError), - max_list(AllSquaredErrors,MaxError), - MSE is SumAllSquaredErrors/Length, - - logger_set_variable(mse_testset,MSE), - logger_set_variable(mse_min_testset,MinError), - logger_set_variable(mse_max_testset,MaxError), - format_learning(2,' (~8f)~n',[MSE]) - ); true - ). - -%======================================================================== -%= Calculates the sigmoid function respectivly the inverse of it -%= warning: applying inv_sigmoid to 0.0 or 1.0 will yield +/-inf -%= -%= +Float, -Float -%======================================================================== - -sigmoid(T,Sig) :- - problog_flag(sigmoid_slope,Slope), - Sig is 1/(1+exp(-T*Slope)). - -inv_sigmoid(T,InvSig) :- - problog_flag(sigmoid_slope,Slope), - InvSig is -log(1/T-1)/Slope. - - - - - - -%======================================================================== -%= Perform one iteration of gradient descent -%= -%= assumes that everything is initialized, if the current values -%= of query_probability/2 and query_gradient/4 are not up to date -%= they will be recalculated -%= finally, the values_correct/0 is retracted to signal that the -%= probabilities of the examples have to be recalculated -%======================================================================== - -save_old_probabilities :- - ( % go over all tunable facts - tunable_fact(FactID,_), - - ( - continuous_fact(FactID) - -> - - ( - get_continuous_fact_parameters(FactID,gaussian(OldMu,OldSigma)), - atomic_concat(['old_mu_',FactID],Key), - atomic_concat(['old_sigma_',FactID],Key2), - bb_put(Key,OldMu), - bb_put(Key2,OldSigma) - ); - ( - get_fact_probability(FactID,OldProbability), - atomic_concat(['old_prob_',FactID],Key), - bb_put(Key,OldProbability) - ) - ), - - fail; % go to next tunable fact - true - ). - - - -forget_old_probabilities :- - ( % go over all tunable facts - tunable_fact(FactID,_), - ( - continuous_fact(FactID) - -> - ( - atomic_concat(['old_mu_',FactID],Key), - atomic_concat(['old_sigma_',FactID],Key2), - atomic_concat(['grad_mu_',FactID],Key3), - atomic_concat(['grad_sigma_',FactID],Key4), - bb_delete(Key,_), - bb_delete(Key2,_), - bb_delete(Key3,_), - bb_delete(Key4,_) - ); - ( - atomic_concat(['old_prob_',FactID],Key), - atomic_concat(['grad_',FactID],Key2), - bb_delete(Key,_), - bb_delete(Key2,_) - ) - ), - - fail; % go to next tunable fact - true - ). - -add_gradient(Learning_Rate) :- - ( % go over all tunable facts - tunable_fact(FactID,_), - ( - continuous_fact(FactID) - -> - ( - atomic_concat(['old_mu_',FactID],Key), - atomic_concat(['old_sigma_',FactID],Key2), - atomic_concat(['grad_mu_',FactID],Key3), - atomic_concat(['grad_sigma_',FactID],Key4), - - bb_get(Key,Old_Mu), - bb_get(Key2,Old_Sigma), - bb_get(Key3,Grad_Mu), - bb_get(Key4,Grad_Sigma), - - Mu is Old_Mu -Learning_Rate* Grad_Mu, - Sigma is exp(log(Old_Sigma) -Learning_Rate* Grad_Sigma), - - set_continuous_fact_parameters(FactID,gaussian(Mu,Sigma)) - ); - ( - atomic_concat(['old_prob_',FactID],Key), - atomic_concat(['grad_',FactID],Key2), - - bb_get(Key,OldProbability), - bb_get(Key2,GradValue), - - inv_sigmoid(OldProbability,OldValue), - NewValue is OldValue -Learning_Rate*GradValue, - sigmoid(NewValue,NewProbability), - - % Prevent "inf" by using values too close to 1.0 - Prob_Secure is min(0.999999999,max(0.000000001,NewProbability)), - set_fact_probability(FactID,Prob_Secure) - ) - ), - - fail; % go to next tunable fact - true - ), - retractall(values_correct). - - -gradient_descent :- - format_learning(2,'Gradient ',[]), - - save_old_probabilities, - update_values, - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % start set gradient to zero - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - ( % go over all tunable facts - - tunable_fact(FactID,_), - ( - continuous_fact(FactID) - -> - - ( - atomic_concat(['grad_mu_',FactID],Key), - atomic_concat(['grad_sigma_',FactID],Key2), - bb_put(Key,0.0), - bb_put(Key2,0.0) - ); - ( - atomic_concat(['grad_',FactID],Key), - bb_put(Key,0.0) - ) - ), - - fail; % go to next tunable fact - - true - ), - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % stop gradient to zero - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - !, - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % start calculate gradient - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - bb_put(mse_train_sum, 0.0), - bb_put(mse_train_min, 0.0), - bb_put(mse_train_max, 0.0), - - problog_flag(alpha,Alpha), - logger_set_variable(alpha,Alpha), - example_count(Example_Count), - - ( % go over all training examples - current_predicate(user:example/4), - user:example(QueryID,_Query,QueryProb,Type), - once(update_query(QueryID,'.',all)), - query_probability(QueryID,BDDProb), - ( - QueryProb=:=0.0 - -> - Y2=Alpha; - Y2=1.0 - ), - ( - (Type == '='; (Type == '<', BDDProb>QueryProb); (Type=='>',BDDProb - Y is Y2*2/Example_Count * (BDDProb-QueryProb); - Y=0.0 - ), - - - % first do the calculations for the MSE on training set - ( - (Type == '='; (Type == '<', BDDProb>QueryProb); (Type=='>',BDDProb - Squared_Error is (BDDProb-QueryProb)**2; - Squared_Error=0.0 - ), - - bb_get(mse_train_sum,Old_MSE_Train_Sum), - bb_get(mse_train_min,Old_MSE_Train_Min), - bb_get(mse_train_max,Old_MSE_Train_Max), - New_MSE_Train_Sum is Old_MSE_Train_Sum+Squared_Error, - New_MSE_Train_Min is min(Old_MSE_Train_Min,Squared_Error), - New_MSE_Train_Max is max(Old_MSE_Train_Max,Squared_Error), - bb_put(mse_train_sum,New_MSE_Train_Sum), - bb_put(mse_train_min,New_MSE_Train_Min), - bb_put(mse_train_max,New_MSE_Train_Max), - - - - ( % go over all tunable facts - tunable_fact(FactID,_), - ( - continuous_fact(FactID) - -> - - ( - atomic_concat(['grad_mu_',FactID],Key), - atomic_concat(['grad_sigma_',FactID],Key2), - - % if the following query fails, - % it means, the fact is not used in the proof - % of QueryID, and the gradient is 0.0 and will - % not contribute to NewValue either way - % DON'T FORGET THIS IF YOU CHANGE SOMETHING HERE! - query_gradient(QueryID,FactID,mu,GradValueMu), - query_gradient(QueryID,FactID,sigma,GradValueSigma), - - bb_get(Key,OldValueMu), - bb_get(Key2,OldValueSigma), - - NewValueMu is OldValueMu + Y*GradValueMu, - NewValueSigma is OldValueSigma + Y*GradValueSigma, - - bb_put(Key,NewValueMu), - bb_put(Key2,NewValueSigma) - ); - ( - atomic_concat(['grad_',FactID],Key), - - % if the following query fails, - % it means, the fact is not used in the proof - % of QueryID, and the gradient is 0.0 and will - % not contribute to NewValue either way - % DON'T FORGET THIS IF YOU CHANGE SOMETHING HERE! - query_gradient(QueryID,FactID,p,GradValue), - - bb_get(Key,OldValue), - NewValue is OldValue + Y*GradValue, - bb_put(Key,NewValue) - ) - ), - - fail; % go to next fact - true - ), - - once(update_query_cleanup(QueryID)), - fail; % go to next training example - true - ), - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % stop calculate gradient - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - !, - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % start statistics on gradient - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - findall(V, ( - tunable_fact(FactID,_), - atomic_concat(['grad_',FactID],Key), - bb_get(Key,V) - ),Gradient_Values), - - ( - Gradient_Values==[] - -> - ( - logger_set_variable(gradient_mean,0.0), - logger_set_variable(gradient_min,0.0), - logger_set_variable(gradient_max,0.0) - ); - ( - sum_list(Gradient_Values,GradSum), - max_list(Gradient_Values,GradMax), - min_list(Gradient_Values,GradMin), - length(Gradient_Values,GradLength), - GradMean is GradSum/GradLength, - - logger_set_variable(gradient_mean,GradMean), - logger_set_variable(gradient_min,GradMin), - logger_set_variable(gradient_max,GradMax) - ) - ), - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % stop statistics on gradient - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - - bb_delete(mse_train_sum,MSE_Train_Sum), - bb_delete(mse_train_min,MSE_Train_Min), - bb_delete(mse_train_max,MSE_Train_Max), - MSE is MSE_Train_Sum/Example_Count, - - logger_set_variable(mse_trainingset,MSE), - logger_set_variable(mse_min_trainingset,MSE_Train_Min), - logger_set_variable(mse_max_trainingset,MSE_Train_Max), - - format_learning(2,'~n',[]), - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % start add gradient to current probabilities - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - ( - problog_flag(line_search,false) - -> - problog_flag(learning_rate,LearningRate); - lineSearch(LearningRate,_) - ), - format_learning(3,'learning rate:~8f~n',[LearningRate]), - add_gradient(LearningRate), - logger_set_variable(learning_rate,LearningRate), - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % stop add gradient to current probabilities - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - !, - forget_old_probabilities. - -%======================================================================== -%= -%= -%======================================================================== - -line_search_evaluate_point(Learning_Rate,MSE) :- - add_gradient(Learning_Rate), - format_learning(2,'Line search (h=~8f) ',[Learning_Rate]), - mse_trainingset_only_for_linesearch(MSE). - - -lineSearch(Final_X,Final_Value) :- - - % Get Parameters for line search - problog_flag(line_search_tolerance,Tol), - problog_flag(line_search_tau,Tau), - problog_flag(line_search_interval,(A,B)), - - format_learning(3,'Line search in interval (~4f,~4f)~n',[A,B]), - - % init values - Acc is Tol * (B-A), - InitRight is A + Tau*(B-A), - InitLeft is A + B - InitRight, - - line_search_evaluate_point(A,Value_A), - line_search_evaluate_point(B,Value_B), - line_search_evaluate_point(InitRight,Value_InitRight), - line_search_evaluate_point(InitLeft,Value_InitLeft), - - bb_put(line_search_a,A), - bb_put(line_search_b,B), - bb_put(line_search_left,InitLeft), - bb_put(line_search_right,InitRight), - - bb_put(line_search_value_a,Value_A), - bb_put(line_search_value_b,Value_B), - bb_put(line_search_value_left,Value_InitLeft), - bb_put(line_search_value_right,Value_InitRight), - - bb_put(line_search_iteration,1), - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - %%%% BEGIN BACK TRACKING - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - ( - repeat, - - bb_get(line_search_iteration,Iteration), - bb_get(line_search_a,Ak), - bb_get(line_search_b,Bk), - bb_get(line_search_left,Left), - bb_get(line_search_right,Right), - - bb_get(line_search_value_a,Fl), - bb_get(line_search_value_b,Fr), - bb_get(line_search_value_left,FLeft), - bb_get(line_search_value_right,FRight), - - ( - % check for infinity, if there is, go to the left - ( FLeft >= FRight, \+ FLeft = (+inf), \+ FRight = (+inf) ) - -> - ( - AkNew=Left, - FlNew=FLeft, - LeftNew=Right, - FLeftNew=FRight, - RightNew is AkNew + Bk - LeftNew, - line_search_evaluate_point(RightNew,FRightNew), - BkNew=Bk, - FrNew=Fr - ); - ( - BkNew=Right, - FrNew=FRight, - RightNew=Left, - FRightNew=FLeft, - LeftNew is Ak + BkNew - RightNew, - - line_search_evaluate_point(LeftNew,FLeftNew), - AkNew=Ak, - FlNew=Fl - ) - ), - - Next_Iteration is Iteration + 1, - - bb_put(line_search_iteration,Next_Iteration), - - bb_put(line_search_a,AkNew), - bb_put(line_search_b,BkNew), - bb_put(line_search_left,LeftNew), - bb_put(line_search_right,RightNew), - - bb_put(line_search_value_a,FlNew), - bb_put(line_search_value_b,FrNew), - bb_put(line_search_value_left,FLeftNew), - bb_put(line_search_value_right,FRightNew), - - % is the search interval smaller than the tolerance level? - BkNew-AkNew0, - !. -line_search_postcheck(V,X,V,X) :- - problog_flag(line_search_never_stop,false), - !. -line_search_postcheck(_,_, LLH, FinalPosition) :- - problog_flag(line_search_tolerance,Tolerance), - problog_flag(line_search_interval,(Left,Right)), - - - Offset is (Right - Left) * Tolerance, - - bb_put(line_search_offset,Offset), - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - ( - - repeat, - - bb_get(line_search_offset,OldOffset), - NewOffset is OldOffset * Tolerance, - bb_put(line_search_offset,NewOffset), - - Position is Left + NewOffset, - line_search_evaluate_point(Position,LLH), - bb_put(line_search_llh,LLH), - - write(logAtom(lineSearchPostCheck(Position,LLH))),nl, - - - \+ LLH = (+inf), - ! - ), % cut away choice point from repeat - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - - bb_delete(line_search_llh,LLH), - bb_delete(line_search_offset,FinalOffset), - FinalPosition is Left + FinalOffset. - - - -my_5_min(V1,V2,V3,V4,V5,F1,F2,F3,F4,F5,VMin,FMin) :- - ( - V1 - (VTemp1=V1,FTemp1=F1); - (VTemp1=V2,FTemp1=F2) - ), - ( - V3 - (VTemp2=V3,FTemp2=F3); - (VTemp2=V4,FTemp2=F4) - ), - ( - VTemp1 - (VTemp3=VTemp1,FTemp3=FTemp1); - (VTemp3=VTemp2,FTemp3=FTemp2) - ), - ( - VTemp3 - (VMin=VTemp3,FMin=FTemp3); - (VMin=V5,FMin=F5) - ). - - -%======================================================================== -%= set the alpha parameter to the value -%= # positive training examples / # negative training examples -%= -%= training example is positive if P(e)=1 -%= training example is negative if P(e)=0 -%= -%= if there are training example with 00, - !, - set_problog_flag(alpha,1.0). -auto_alpha :- - findall(1,(user:example(_,_,P,=),P=:=1.0),Pos), - findall(0,(user:example(_,_,P,=),P=:=0.0),Neg), - length(Pos,NP), - length(Neg,NN), - Alpha is NP/NN, - set_problog_flag(alpha,Alpha). - - - -%======================================================================== -%= initialize the logger module and set the flags for learning -%= don't change anything here! use set_problog_flag/2 instead -%======================================================================== - -init_flags :- - prolog_file_name('queries',Queries_Folder), % get absolute file name for './queries' - prolog_file_name('output',Output_Folder), % get absolute file name for './output' - problog_define_flag(bdd_directory, problog_flag_validate_directory, 'directory for BDD scripts', Queries_Folder,learning_general), - problog_define_flag(output_directory, problog_flag_validate_directory, 'directory for logfiles etc', Output_Folder,learning_general,flags:learning_output_dir_handler), - problog_define_flag(log_frequency, problog_flag_validate_posint, 'log results every nth iteration', 1, learning_general), - problog_define_flag(rebuild_bdds, problog_flag_validate_nonegint, 'rebuild BDDs every nth iteration', 0, learning_general), - problog_define_flag(reuse_initialized_bdds,problog_flag_validate_boolean, 'Reuse BDDs from previous runs',false, learning_general), - problog_define_flag(check_duplicate_bdds,problog_flag_validate_boolean,'Store intermediate results in hash table',true,learning_general), - problog_define_flag(init_method,problog_flag_validate_dummy,'ProbLog predicate to search proofs',(Query,Probability,BDDFile,ProbFile,problog_kbest_save(Query,100,Probability,_Status,BDDFile,ProbFile)),learning_general,flags:learning_init_handler), - problog_define_flag(alpha,problog_flag_validate_number,'weight of negative examples (auto=n_p/n_n)',auto,learning_general,flags:auto_handler), - problog_define_flag(sigmoid_slope,problog_flag_validate_posnumber,'slope of sigmoid function',1.0,learning_general), - - problog_define_flag(learning_rate,problog_flag_validate_posnumber,'Default learning rate (If line_search=false)',examples,learning_line_search,flags:examples_handler), - problog_define_flag(line_search, problog_flag_validate_boolean,'estimate learning rate by line search',false,learning_line_search), - problog_define_flag(line_search_never_stop, problog_flag_validate_boolean,'make tiny step if line search returns 0',true,learning_line_search), - problog_define_flag(line_search_tau, problog_flag_validate_indomain_0_1_open,'tau value for line search',0.618033988749,learning_line_search), - problog_define_flag(line_search_tolerance,problog_flag_validate_posnumber,'tolerance value for line search',0.05,learning_line_search), - problog_define_flag(line_search_interval, problog_flag_validate_dummy,'interval for line search',(0,100),learning_line_search,flags:linesearch_interval_handler). - - -init_logger :- - logger_define_variable(iteration, int), - logger_define_variable(duration,time), - logger_define_variable(mse_trainingset,float), - logger_define_variable(mse_min_trainingset,float), - logger_define_variable(mse_max_trainingset,float), - logger_define_variable(mse_testset,float), - logger_define_variable(mse_min_testset,float), - logger_define_variable(mse_max_testset,float), - logger_define_variable(gradient_mean,float), - logger_define_variable(gradient_min,float), - logger_define_variable(gradient_max,float), - logger_define_variable(ground_truth_diff,float), - logger_define_variable(ground_truth_mindiff,float), - logger_define_variable(ground_truth_maxdiff,float), - logger_define_variable(learning_rate,float), - logger_define_variable(alpha,float). - -:- initialization(init_flags). -:- initialization(init_logger). -