%%% -*- 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 ]). % 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)). % 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'). :- use_module('problog/lbdd'). % 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. %======================================================================== %= 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]), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % set learning rate and alpha %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( problog_flag(learning_rate,examples) -> set_problog_flag(learning_rate,TrainingExampleCount); true ), ( problog_flag(alpha,auto) -> ( (user:example(_,_,P,_),P<1,P>0) -> set_problog_flag(alpha,1.0) ; ( succeeds_n_times((user:example(_,_,P,=),P=:=1.0),Pos_Count), succeeds_n_times((user:example(_,_,P,=),P=:=0.0),Neg_Count), Alpha is Pos_Count/Neg_Count, set_problog_flag(alpha,Alpha) ) ) ; true ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 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,(Goal,N,Bdd,graph2bdd(X,Y,N,Bdd))), !. */ set_default_gradient_method :- set_problog_flag(init_method,(Query,1,BDD, problog_kbest_as_bdd(user:Query,1,BDD))). %======================================================================== %= 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 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% b_setval(problog_required_keep_ground_ids,false), problog_flag(libbdd_init_method,(Query,Bdd,Call)), !, Bdd = bdd(Dir, Tree, MapList), % trace, once(Call), rb_new(H0), maplist_to_hash(MapList, H0, Hash), Tree \= [], % writeln(Dir:Tree:MapList), tree_to_grad(Tree, Hash, [], Grad). 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 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% b_setval(problog_required_keep_ground_ids,false), problog_flag(init_method,(Query,N,Bdd,_)), !, Bdd = bdd(Dir, Tree, MapList), ( user:graph2bdd(Query,N,Bdd) -> rb_new(H0), maplist_to_hash(MapList, H0, Hash), Tree \= [], tree_to_grad(Tree, Hash, [], Grad) ; Bdd = bdd(-1,[],[]), Grad=[] ), recordz(QueryID,bdd(Dir, Grad, MapList),_). init_one_query(_QueryID,_Query,_Type) :- throw(unsupported_init_method). %======================================================================== %= 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,p,Value) :- !, query_gradient_intern(QueryID,Fact,p,Value). 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) :- 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',[MSE]). %======================================================================== %= 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 :- problog_flag(continous_facts, true), !, forall(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) ) ) ). save_old_probabilities :- forall(tunable_fact(FactID,_), ( get_fact_probability(FactID,OldProbability), atomic_concat(['old_prob_',FactID],Key), bb_put(Key,OldProbability) ) ). save_old_probabilities :- forall(tunable_fact(FactID,_), ( get_fact_probability(FactID,OldProbability), atomic_concat(['old_prob_',FactID],Key), bb_put(Key,OldProbability) ) ). forget_old_probabilities :- problog_flag(continous_facts, true), !, forall(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,_) ) ) ). forget_old_probabilities :- forall(tunable_fact(FactID,_), ( atomic_concat(['old_prob_',FactID],Key), atomic_concat(['grad_',FactID],Key2), bb_delete(Key,_), bb_delete(Key2,_) ) ). add_gradient(Learning_Rate) :- problog_flag(continous_facts, true), !, forall(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), %writeln(FactID:OldValue +Learning_Rate*GradValue), 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) ) ) ), retractall(values_correct). add_gradient(Learning_Rate) :- forall(tunable_fact(FactID,_), ( atomic_concat(['old_prob_',FactID],Key), atomic_concat(['grad_',FactID],Key2), bb_get(Key,OldProbability), bb_get(Key2,GradValue), inv_sigmoid(OldProbability,OldValue), %writeln(FactID:OldValue +Learning_Rate*GradValue), 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) ) ), retractall(values_correct). % vsc: avoid silly search gradient_descent :- current_iteration(Iteration), create_training_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,'Gradient ',[]), save_old_probabilities, update_values, reset_gradients, compute_gradients(Handle). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start set gradient to zero %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% reset_gradients :- problog_flag(continous_facts, true), !, forall(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) ) ) ). reset_gradients :- forall(tunable_fact(FactID,_), ( atomic_concat(['grad_',FactID],Key), bb_put(Key,0.0) ) ). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % stop gradient to zero %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start calculate gradient %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% compute_gradients(Handle) :- bb_put(mse_train_sum, 0.0), bb_put(mse_train_min, 0.0), bb_put(mse_train_max, 0.0), bb_put(llh_training_queries, 0.0), problog_flag(alpha,Alpha), logger_set_variable(alpha,Alpha), example_count(Example_Count), forall(user:example(QueryID,Query,QueryProb,Type), ( once(update_query(QueryID,'.',all)), query_probability(QueryID,BDDProb), format(Handle,'ex(~q,train,~q,~q,~10f,~10f).~n',[Iteration,QueryID,Query,QueryProb,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), bb_get(llh_training_queries,Old_LLH_Training_Queries), 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), New_LLH_Training_Queries is Old_LLH_Training_Queries+log(BDDProb), 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), bb_put(llh_training_queries,New_LLH_Training_Queries), ( % go over all tunable facts query_gradient(QueryID,FactID,p,GradValue), 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! %writeln(u:QueryID:FactID:Y:GradValue), bb_get(Key,OldValue), NewValue is OldValue - Y*GradValue, bb_put(Key,NewValue), fail; % go to next fact true ), ( 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), fail ; true ), once(update_query_cleanup(QueryID)) )), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % stop calculate gradient %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% !, close(Handle), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 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), bb_delete(llh_training_queries,LLH_Training_Queries), 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), logger_set_variable(llh_training_queries,LLH_Training_Queries), 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 B - Tau*(B-A), 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), Parameters=ls(A,B,InitLeft,InitRight,Value_A,Value_B,Value_InitLeft,Value_InitRight,1), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%% BEGIN BACK TRACKING %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( repeat, Parameters=ls(Ak,Bk,Left,Right,Fl,Fr,FLeft,FRight,Iteration), ( % 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 Left + Bk - Right, line_search_evaluate_point(RightNew,FRightNew), BkNew=Bk, FrNew=Fr, Interval_Size is Bk-Left ); ( BkNew=Right, FrNew=FRight, RightNew=Left, FRightNew=FLeft, LeftNew is Ak + Right - Left, line_search_evaluate_point(LeftNew,FLeftNew), AkNew=Ak, FlNew=Fl, Interval_Size is Right-Ak ) ), Next_Iteration is Iteration + 1, nb_setarg(9,Parameters,Next_Iteration), nb_setarg(1,Parameters,AkNew), nb_setarg(2,Parameters,BkNew), nb_setarg(3,Parameters,LeftNew), nb_setarg(4,Parameters,RightNew), nb_setarg(5,Parameters,FlNew), nb_setarg(6,Parameters,FrNew), nb_setarg(7,Parameters,FLeftNew), nb_setarg(8,Parameters,FRightNew), % is the search interval smaller than the tolerance level? Interval_Size0, !. 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) ). %======================================================================== %= 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(libbdd_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(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), writeln(1), 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).