%%% -*- Mode: Prolog; -*- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % $Date: 2011-04-21 14:18:59 +0200 (Thu, 21 Apr 2011) $ % $Revision: 6364 $ % % 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, Vitor Santos Costa % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % Artistic License 2.0 % % Copyright (c) 2000-2006, The Perl Foundation. % % Everyone is permitted to copy and distribute verbatim copies of this % license document, but changing it is not allowed. Preamble % % This license establishes the terms under which a given free software % Package may be copied, modified, distributed, and/or % redistributed. 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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, sigmoid/3, inv_sigmoid/3 ]). % 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), [member/2,max_list/2, min_list/2, sum_list/2]). :- use_module(library(system), [file_exists/1, shell/2]). :- use_module(library(rbtrees)). :- use_module(library(lbfgs)). % load our own modules :- reexport(problog). :- use_module('problog/logger'). :- use_module('problog/flags'). :- use_module('problog/os'). :- use_module('problog/print_learning'). :- use_module('problog/utils_lbdd'). :- use_module('problog/utils'). :- use_module('problog/tabling'). % 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). % used to identify queries which have identical proofs :- dynamic(query_is_similar/2). :- dynamic(query_md5/3). :- multifile(user:example/4). :- multifile(user:problog_discard_example/1). user:example(A,B,C,=) :- current_predicate(user:example/3), user:example(A,B,C), \+ user:problog_discard_example(B). :- multifile(user:test_example/4). user:test_example(A,B,C,=) :- current_predicate(user:test_example/3), user:test_example(A,B,C), \+ user:problog_discard_example(B). %======================================================================== %= store the facts with the learned probabilities to a file %======================================================================== save_model:- current_iteration(Iteration), create_factprobs_file_name(Iteration,Filename), export_facts(Filename). %======================================================================== %= find out whether some example IDs are used more than once %= if so, complain and stop %= %======================================================================== check_examples :- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Check example IDs %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( (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 ), ( (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 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( (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 ), ( (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 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( ( ( user:example(ID,QueryA,_,_), user:example(ID,QueryB,_,_), QueryA \= QueryB ) ; ( user:test_example(ID,QueryA,_,_), user:test_example(ID,QueryB,_,_), QueryA \= QueryB ); ( 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(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_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, % empty_output_directory, logger_write_header, format_learning(1,'Initializing everything~n',[]), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Check, if continuous facts are used. % if yes, switch to problog_exact % continuous facts are not supported yet. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% set_default_gradient_method, ( problog_flag(continuous_facts, true ) -> problog_flag(init_method,(_,_,_,_,OldCall)), ( ( continuous_fact(_), OldCall\=problog_exact_save(_,_,_,_,_) ) -> ( format_learning(2,'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 ) ; problog_tabled(_) -> ( format_learning(2,'Theory uses tabling.~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 ), succeeds_n_times(user:test_example(_,_,_,_),TestExampleCount), format_learning(3,'~q test examples~n',[TestExampleCount]), succeeds_n_times(user:example(_,_,_,_),TrainingExampleCount), assertz(example_count(TrainingExampleCount)), format_learning(3,'~q training examples~n',[TrainingExampleCount]), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % build BDD script for every example %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% once(init_queries), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % done %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% assertz(current_iteration(0)), assertz(learning_initialized), format_learning(1,'~n',[]). empty_bdd_directory :- current_key(_,I), integer(I), recorded(I,bdd(_,_,_),R), erase(R), fail. empty_bdd_directory. set_default_gradient_method :- problog_flag(continuous_facts, true), !, problog_flag(init_method,OldMethod), format_learning(2,'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))). set_default_gradient_method :- problog_tabled(_), problog_flag(fast_proofs,false), !, format_learning(2,'Theory uses tabling.~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))). %set_default_gradient_method :- % problog_flag(init_method,(gene(X,Y),N,Bdd,graph2bdd(X,Y,N,Bdd))), % !. set_default_gradient_method. %======================================================================== %= 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',[]), forall(user:test_example(ID,Query,_Prob,_),init_one_query(ID,Query,test)), forall(user:example(ID,Query,_Prob,_),init_one_query(ID,Query,training)). bdd_input_file(Filename) :- problog_flag(output_directory,Dir), concat_path_with_filename(Dir,'input.txt',Filename). init_one_query(QueryID,Query,Type) :- % format_learning(3,' ~q example ~q: ~q~n',[Type,QueryID,Query]), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % if BDD file does not exist, call ProbLog %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( recorded(QueryID, _, _) -> format_learning(3,' Reuse existing BDD ~q~n~n',[QueryID]) ; b_setval(problog_required_keep_ground_ids,false), (QueryID mod 100 =:= 0 -> writeln(QueryID) ; true), problog_flag(init_method,(Query,N,Bdd,graph2bdd(X,Y,N,Bdd))), Query =.. [_,X,Y] -> Bdd = bdd(Dir, Tree, MapList), ( graph2bdd(X,Y,N,Bdd) -> rb_new(H0), maplist_to_hash(MapList, H0, Hash), tree_to_grad(Tree, Hash, [], Grad) % ; % Bdd = bdd(-1,[],[]), % Grad=[] ), write('.'), recordz(QueryID,bdd(Dir, Grad, MapList),_) ; problog_flag(init_method,(Query,NOf,Bdd,problog_kbest_as_bdd(Call,NOf,Bdd))) -> b_setval(problog_required_keep_ground_ids,false), rb_new(H0), strip_module(Call,_,Goal), !, Bdd = bdd(Dir, Tree, MapList), % trace, problog:problog_kbest_as_bdd(Goal,NOf,Bdd), maplist_to_hash(MapList, H0, Hash), Tree \= [], %put_code(0'.), tree_to_grad(Tree, Hash, [], Grad), recordz(QueryID,bdd(Dir, Grad, MapList),_) ; problog_flag(init_method,(Query,NOf,Bdd,Call)) -> b_setval(problog_required_keep_ground_ids,false), rb_new(H0), Bdd = bdd(Dir, Tree, MapList), % trace, problog:Call, maplist_to_hash(MapList, H0, Hash), Tree \= [], %put_code(0'.), tree_to_grad(Tree, Hash, [], Grad), recordz(QueryID,bdd(Dir, Grad, MapList),_) ). %======================================================================== %= %= %= %======================================================================== query_probability(QueryID,Prob) :- Prob <== qp[QueryID]. %======================================================================== %= %= %= %======================================================================== % FIXME ground_truth_difference :- findall(Diff,(tunable_fact(FactID,GroundTruth), \+continuous_fact(FactID), \+ var(GroundTruth), %% get_fact_probability(FactID,Prob), Prob <== p[FactID], 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) :- update_values, example_count(Example_Count), bb_put(error_train_line_search,0.0), forall(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 ( bb_get(error_train_line_search,Old_Error), New_Error is Old_Error + (CurrentProb-QueryProb)**2, bb_put(error_train_line_search,New_Error) );true ) ) ), bb_delete(error_train_line_search,Error), MSE is Error/Example_Count, format_learning(3,' (~8f)~n',[MSE]), retractall(values_correct). mse_testset :- current_iteration(Iteration), create_test_predictions_file_name(Iteration,File_Name), open(File_Name,'write',Handle), format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%~n",[]), format(Handle,"% Iteration, train/test, QueryID, Query, GroundTruth, Prediction %~n",[]), format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%~n",[]), format_learning(2,'MSE_Test ',[]), update_values, bb_put(llh_test_queries,0.0), findall(SquaredError, (user:test_example(QueryID,Query,TrueQueryProb,Type), once(update_query(QueryID,'+',probability)), query_probability(QueryID,CurrentProb), format(Handle,'ex(~q,test,~q,~q,~10f,~10f).~n',[Iteration,QueryID,Query,TrueQueryProb,CurrentProb]), once(update_query_cleanup(QueryID)), ( (Type == '='; (Type == '<', CurrentProb>QueryProb); (Type=='>',CurrentProb SquaredError is (CurrentProb-TrueQueryProb)**2; SquaredError = 0.0 ), bb_get(llh_test_queries,Old_LLH_Test_Queries), New_LLH_Test_Queries is Old_LLH_Test_Queries+log(CurrentProb), bb_put(llh_test_queries,New_LLH_Test_Queries) ), AllSquaredErrors), close(Handle), bb_delete(llh_test_queries,LLH_Test_Queries), length(AllSquaredErrors,Length), ( Length>0 -> ( sum_list(AllSquaredErrors,SumAllSquaredErrors), min_list(AllSquaredErrors,MinError), max_list(AllSquaredErrors,MaxError), MSE is SumAllSquaredErrors/Length );( MSE=0.0, MinError=0.0, MaxError=0.0 ) ), logger_set_variable(mse_testset,MSE), logger_set_variable(mse_min_testset,MinError), logger_set_variable(mse_max_testset,MaxError), logger_set_variable(llh_test_queries,LLH_Test_Queries), format_learning(2,' (~8f)~n',[M]). %======================================================================== %= 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,Slope,Sig) :- IN <== T, OUT is 1/(1+exp(-IN*Slope)), Sig <== OUT. inv_sigmoid(T,Slope,InvSig) :- InvSig <== -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 :- old_prob <== p. % vsc: avoid silly search gradient_descent :- current_iteration(Iteration), create_training_predictions_file_name(Iteration,File_Name), Handle = user_error, format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%~n",[]), format(Handle,"% Iteration, train/test, QueryID, Query, GroundTruth, Prediction %~n",[]), format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%~n",[]), format_learning(2,'Gradient ',[]), findall(FactID,tunable_fact(FactID,GroundTruth),L), length(L,N), % leash(0),trace, lbfgs_initialize(N,X,0,Solver), N1 is N-1, forall(tunable_fact(FactID,GroundTruth), (X[FactID] <== 0.5)), problog_flag(sigmoid_slope,Slope), lbfgs_run(Solver,BestF), format('~2nOptimization done~nWe found a minimum ~4f.~n',[BestF]), forall(tunable_fact(FactID,GroundTruth), set_tunable(FactID,GroundTruth,X)), lbfgs_finalize(Solver). set_tunable(I, GroundTruth,P) :- Pr <== P[I], get_fact(I,Source), format('fact(~d, ~q, ~4f, ~4f).~n',[I,Source,GroundTruth,Pr]), set_fact_probability(I,Pr). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start calculate gradient %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% user:evaluate(LLH_Training_Queries, X,Grad,N,_,_) :- Handle = user_error, problog_flag(sigmoid_slope,Slope), Probs = X, N1 is N-1, forall(between(0,N1,I), (Grad[I] <== 0.0) %, sigmoid(X[I],Slope,Probs[I]) ) ), findall(LL, compute_grad(N, X, Grad, Probs, Slope, Handle, LL), LLs ), sum_list(LLs,LLH_Training_Queries), forall(tunable_fact(FactID,GroundTruth), (Z<==X[FactID],W<==Grad[FactID],writeln(FactID:(W->Z)))). compute_grad(N, X, Grad, Probs, Slope, Handle, LL) :- user:example(QueryID,_Query,QueryProb,_), recorded(QueryID,BDD,_), BDD = bdd(_Dir, _GradTree, MapList), bind_maplist(MapList, Slope, Probs), %writeln( qprobability(BDD,Slope,BDDProb) ), qprobability(BDD,Slope,BDDProb), %writeln( gradientpair(BDD,Slope,BDDProb, QueryProb, Grad) ), gradientpair(BDD,Slope,BDDProb, QueryProb, Grad), LL is (((BDDProb)-(QueryProb))**2). gradientpair(BDD,Slope,BDDProb, QueryProb, Grad) :- qgradient(BDD, Slope, FactID, GradValue), % writeln(FactID), G0 <== Grad[FactID], %writeln( GN is G0-GradValue*(QueryProb-BDDProb)), GN is G0-GradValue*(QueryProb-BDDProb), %writeln(FactID:(G0->GN)), Grad[FactID] <== GN. gradientpair(_BDD,_Slope,_BDDProb, _Grad). qprobability(bdd(Dir, Tree, MapList), Slope, Prob) :- /* query_probability(21,6.775948e-01). */ run_sp(Tree, Slope, 1.0, Prob0), (Dir == 1 -> Prob0 = Prob ; Prob is 1.0-Prob0). qgradient(bdd(Dir, Tree, MapList), Slope, I, Grad) :- member(I-_, MapList), run_grad(Tree, I, Slope, 0.0, Grad0), ( Dir = 1 -> Grad = Grad0 ; Grad is -Grad0). % writeln(grad(QueryID:I:Grad)), % assert(query_gradient_intern(QueryID,I,p,Grad)), % fail. %gradient(QueryID, g, Slope) :- % gradient(QueryID, l, Slope). maplist_to_hash([], H0, H0). maplist_to_hash([I-V|MapList], H0, Hash) :- rb_insert(H0, V, I, H1), maplist_to_hash(MapList, H1, Hash). tree_to_grad([], _, Grad, Grad). tree_to_grad([Node|Tree], H, Grad0, Grad) :- node_to_gradient_node(Node, H, GNode), tree_to_grad(Tree, H, [GNode|Grad0], Grad). node_to_gradient_node(pp(P-G,X,L,R), H, gnodep(P,G,X,Id,PL,GL,PR,GR)) :- rb_lookup(X,Id,H), (L == 1 -> GL=0, PL=1 ; L == 0 -> GL = 0, PL=0 ; L = PL-GL), (R == 1 -> GR=0, PR=1 ; R == 0 -> GR = 0, PR=0 ; R = PR-GR). node_to_gradient_node(pn(P-G,X,L,R), H, gnoden(P,G,X,Id,PL,GL,PR,GR)) :- rb_lookup(X,Id,H), (L == 1 -> GL=0, PL=1 ; L == 0 -> GL = 0, PL=0 ; L = PL-GL), (R == 1 -> GR=0, PR=1 ; R == 0 -> GR = 0, PR=0 ; R = PR-GR). run_sp([], _, P0, P0). run_sp(gnodep(P,_G, EP, _Id, PL, _GL, PR, _GR).Tree, Slope, _, PF) :- P is EP*PL+ (1.0-EP)*PR, run_sp(Tree, Slope, P, PF). run_sp(gnoden(P,_G, EP, _Id, PL, _GL, PR, _GR).Tree, Slope, _, PF) :- P is EP*PL + (1.0-EP)*(1.0 - PR), run_sp(Tree, Slope, P, PF). run_grad([], _I, _, G0, G0). run_grad([gnodep(P,G, EP, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :- P is EP*PL+ (1.0-EP)*PR, G0 is EP*GL + (1.0-EP)*GR, % don' t forget the -X ( I == Id -> G is G0+(PL-PR)* EP*(1-EP)*Slope ; G = G0 ), run_grad(Tree, I, Slope, G, GF). run_grad([gnoden(P,G, EP, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :- P is EP*PL + (1.0-EP)*(1.0 - PR), G0 is EP*GL - (1.0 - EP) * GR, ( I == Id -> G is G0+(PL+PR-1)*EP*(1-EP)*Slope ; G = G0 ), run_grad(Tree, I, Slope, G, GF). prob2log(X,Slope,FactID,V) :- get_fact_probability(FactID, V0), inv_sigmoid(V0, Slope, V). log2prob(X,Slope,FactID,V) :- V0 <== X[FactID], sigmoid(V0, Slope, V). bind_maplist([], Slope, X). bind_maplist([Node-Pr|MapList], Slope, X) :- V0 <== X[Node], sigmoid(V0, Slope, V), bind_maplist(MapList, Slope, X). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % stop calculate gradient %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% user:progress(FX,X,_G,X_Norm,G_Norm,Step,_N,Iteration,Ls,0) :- problog_flag(sigmoid_slope,Slope), X0 <== X[0], X1 <== X[1], format('~d. Iteration : (x0,x1)=(~4f,~4f) f(X)=~4f |X|=~4f |X\'|=~4f Step=~4f Ls=~4f~n',[Iteration,X0 ,X1,FX,X_Norm,G_Norm,Step,Ls]). %======================================================================== %= 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,Tree,problog:problog_kbest_as_bdd(Query,100,Tree)),learning_general,flags:learning_libdd_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(continuous_facts,problog_flag_validate_boolean,'support parameter learning of continuous distributions',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), logger_define_variable(llh_training_queries,float), logger_define_variable(llh_test_queries,float). :- initialization(init_flags). :- initialization(init_logger).