%%% -*- Mode: Prolog; -*- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % $Date: 2009-06-17 22:22:00 +0200 (Mi, 17 Jun 2009) $ % $Revision: 1550 $ % % This file is part of ProbLog % http://dtai.cs.kuleuven.be/problog % % Copyright 2009 Katholieke Universiteit Leuven % % Authors: Luc De Raedt, Bernd Gutmann, Angelika Kimmig, % Vitor Santos Costa % % % Main authors of this file: % 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. 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, set_learning_flag/2, save_model/1, problog_help/0, set_problog_flag/2, problog_flag/2, problog_flags/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)). :- use_module(library(random),[random/1]). :- use_module(library(system),[file_exists/1, file_property/2, delete_file/1, make_directory/1, working_directory/2, shell/1, shell/2]). % load our own modules :- use_module(library('problog_learning/logger')). :- use_module(library(problog)). % 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/3. :- dynamic last_mse/1. % used to identify queries which have identical proofs :- dynamic query_is_similar/2. :- dynamic query_md5/2. % used by set_learning_flag :- dynamic init_method/5. :- dynamic rebuild_bdds/1. :- dynamic rebuild_bdds_it/1. :- dynamic reuse_initialized_bdds/1. :- dynamic learning_rate/1. :- dynamic probability_initializer/3. :- dynamic check_duplicate_bdds/1. :- dynamic output_directory/1. :- dynamic query_directory/1. :- dynamic log_frequency/1. :- dynamic alpha/1. :- dynamic sigmoid_slope/1. :- dynamic line_search/1. :- dynamic line_search_tolerance/1. :- dynamic line_search_tau/1. :- dynamic line_search_never_stop/1. :- dynamic line_search_interval/2. %========================================================================== %= You can set some flags and parameters %= %= init_method/5 specifies which ProbLog inference mechanism is used %= to answer queries %= %= %= if rebuild_bdds(true) is set, the bdds are rebuild after %= each N iterations for rebuild_bdds_it(N) %= %= if reuse_initialized_bdds(true) is set, the bdds which are on the %= harddrive from the previous run of LeProbLog are reused. %= do not use this, when you changed the init method in the meantime %= %========================================================================== set_learning_flag(init_method,(Query,Probability,BDDFile,ProbFile,Call)) :- retractall(init_method(_,_,_,_,_)), assert(init_method(Query,Probability,BDDFile,ProbFile,Call)). set_learning_flag(rebuild_bdds,Flag) :- (Flag=true;Flag=false), !, retractall(rebuild_bdds(_)), assert(rebuild_bdds(Flag)). set_learning_flag(rebuild_bdds_it,Flag) :- integer(Flag), retractall(rebuild_bdds_it(_)), assert(rebuild_bdds_it(Flag)). set_learning_flag(reuse_initialized_bdds,Flag) :- (Flag=true;Flag=false), !, retractall(reuse_initialized_bdds(_)), assert(reuse_initialized_bdds(Flag)). set_learning_flag(learning_rate,V) :- (V=examples -> true;(number(V),V>=0)), !, retractall(learning_rate(_)), assert(learning_rate(V)). set_learning_flag(probability_initializer,(FactID,Probability,Query)) :- var(FactID), var(Probability), callable(Query), retractall(probability_initializer(_,_,_)), assert(probability_initializer(FactID,Probability,Query)). set_learning_flag(check_duplicate_bdds,Flag) :- (Flag=true;Flag=false), !, retractall(check_duplicate_bdds(_)), assert(check_duplicate_bdds(Flag)). set_learning_flag(output_directory,Directory) :- ( file_exists(Directory) -> file_property(Directory,type(directory)); make_directory(Directory) ), working_directory(PWD,PWD), atomic_concat([PWD,'/',Directory,'/'],Path), atomic_concat([Directory,'/log.dat'],Logfile), retractall(output_directory(_)), assert(output_directory(Path)), logger_set_filename(Logfile), set_problog_flag(dir,Directory). set_learning_flag(query_directory,Directory) :- ( file_exists(Directory) -> file_property(Directory,type(directory)); make_directory(Directory) ), working_directory(PWD,PWD), atomic_concat([PWD,'/',Directory,'/'],Path), retractall(query_directory(_)), assert(query_directory(Path)). set_learning_flag(log_frequency,Frequency) :- integer(Frequency), Frequency>=0, retractall(log_frequency(_)), assert(log_frequency(Frequency)). set_learning_flag(alpha,Alpha) :- number(Alpha), retractall(alpha(_)), assert(alpha(Alpha)). set_learning_flag(sigmoid_slope,Slope) :- number(Slope), Slope>0, retractall(sigmoid_slope(_)), assert(sigmoid_slope(Slope)). set_learning_flag(line_search,Flag) :- (Flag=true;Flag=false), !, retractall(line_search(_)), assert(line_search(Flag)). set_learning_flag(line_search_tolerance,Number) :- number(Number), Number>0, retractall(line_search_tolerance(_)), assert(line_search_tolerance(Number)). set_learning_flag(line_search_interval,(L,R)) :- number(L), number(R), L0, retractall(line_search_tau(_)), assert(line_search_tau(Number)). set_learning_flag(line_search_never_stop,Flag) :- (Flag=true;Flag=false), !, retractall(line_search_nerver_stop(_)), assert(line_search_never_stop(Flag)). %======================================================================== %= 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(F) :- ( var(F) -> ( current_iteration(Iteration), output_directory(Directory), atomic_concat([Directory,'factprobs_',Iteration,'.pl'],F) );true ), export_facts(F). %======================================================================== %= store the probabilities for all training and test examples %= if F is a variable, a filename based on the current iteration is used %= %======================================================================== save_predictions(F) :- update_values, current_iteration(Iteration), ( var(F) -> ( current_iteration(Iteration), output_directory(Directory), atomic_concat([Directory,'predictions_',Iteration,'.pl'],F) );true ), open(F,'append',Handle), format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",[]), format(Handle,"% Iteration, train/test, QueryID, Query, GroundTruth, Prediction %\n",[]), format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",[]), !, ( % go over all training examples current_predicate(user:example/3), 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 ), ( % go over all test examples current_predicate(user:test_example/3), 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 ), format(Handle,'~3n',[]), close(Handle). %======================================================================== %= find out whether some example IDs are used more than once %= if so, complain and stop %= %======================================================================== check_examples :- ( ( (current_predicate(user:example/3),user:example(ID,_,_), \+ atomic(ID)) ; (current_predicate(user:test_example/3),user:test_example(ID,_,_), \+ atomic(ID)) ) -> ( format(user_error,'The example id of example ~q is not atomic (e.g foo42, 23, bar, ...).~n',[ID]), throw(error(examples)) ); true ), ( ( (current_predicate(user:example/3),user:example(ID,_,P), (\+ number(P); P>1 ; P<0)); (current_predicate(user:test_example/3),user:test_example(ID,_,P), (\+ number(P) ; P>1 ; P<0)) ) -> ( format(user_error,'The example ~q does not have a valid probaility value (~q).~n',[ID,P]), throw(error(examples)) ); true ), ( ( ( current_predicate(user:example/3), user:example(ID,QueryA,_), user:example(ID,QueryB,_), QueryA \= QueryB ) ; ( current_predicate(user:test_example/3), user:test_example(ID,QueryA,_), user:test_example(ID,QueryB,_), QueryA \= QueryB ); ( current_predicate(user:example/3), current_predicate(user:test_example/3), 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 ). %======================================================================== %= 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) :- integer(Iterations), ( current_predicate(user:example/3) -> true; format(user_error,'~n~nWarning: No training examples specified !!!~n~n',[]) ), do_learning_intern(Iterations,-1). do_learning(Iterations,Epsilon) :- integer(Iterations), float(Epsilon), Iterations>0, Epsilon>0.0, ( current_predicate(user:example/3) -> true; format(user_error,'~n~nWarning: No training examples specified !!!~n~n',[]) ), do_learning_intern(Iterations,Epsilon). do_learning_intern(Iterations,Epsilon) :- ( Iterations=0 -> true; ( Iterations>0, % nothing will happen, if we're already initialized init_learning, current_iteration(OldIteration), !, retractall(current_iteration(_)), !, CurrentIteration is OldIteration+1, assert(current_iteration(CurrentIteration)), EndIteration is OldIteration+Iterations, format('~n Iteration ~d of ~d~n',[CurrentIteration,EndIteration]), logger_set_variable(iteration,CurrentIteration), logger_start_timer(duration), gradient_descent, ( (rebuild_bdds(true),rebuild_bdds_it(BDDFreq),0 =:= CurrentIteration mod BDDFreq) -> ( once(delete_all_queries), once(init_queries) ); true ), mse_trainingset, mse_testset, ( last_mse(Last_MSE) -> ( retractall(last_mse(_)), logger_get_variable(mse_trainingset,Current_MSE), assert(last_mse(Current_MSE)), !, MSE_Diff is abs(Last_MSE-Current_MSE) ); ( logger_get_variable(mse_trainingset,Current_MSE), assert(last_mse(Current_MSE)), MSE_Diff is Epsilon+1 ) ), !, logger_stop_timer(duration), once(ground_truth_difference), logger_write_data, log_frequency(Log_Frequency), ( ( Log_Frequency=0; 0 =:= CurrentIteration mod Log_Frequency) -> ( save_predictions(_X), save_model(_Y) ); true ), 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 -> true; ( check_examples, format('Delete previous logs (if existing) from output directory~2n',[]), empty_output_directory, format('Initializing everything~n',[]), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Delete the BDDs from the previous run if they should % not be reused %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( (reuse_initialized_bdds(false);rebuild_bdds(true)) -> delete_all_queries; true ), logger_write_header, logger_start_timer(duration), logger_set_variable(iteration,0), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start count examples %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% bb_put(training_examples,0), ( % go over all training examples current_predicate(user:example/3), user:example(_,_,_), bb_get(training_examples, OldCounter), NewCounter is OldCounter+1, bb_put(training_examples,NewCounter), fail; true ), bb_put(test_examples,0), ( % go over all test examples current_predicate(user:test_example/3), user:test_example(_,_,_), bb_get(test_examples, OldCounter), NewCounter is OldCounter+1, bb_put(test_examples,NewCounter), fail; true ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % stop count examples %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% !, bb_delete(training_examples,TrainingExampleCount), bb_delete(test_examples,TestExampleCount), assert(example_count(TrainingExampleCount)), ( learning_rate(examples) -> set_learning_flag(learning_rate,TrainingExampleCount); true ), learning_rate(Learning_Rate), format('~q training examples found.~n~q test examples found.~nlearning rate=~f~n~n', [TrainingExampleCount,TestExampleCount,Learning_Rate]), format('Generate BDDs for all queries in the training and test set~n',[]), initialize_fact_probabilities, init_queries, format('All Queries have been generated~n',[]), mse_trainingset, mse_testset, !, logger_stop_timer(duration), ground_truth_difference, logger_write_data, assert(current_iteration(0)), assert(learning_initialized), save_model(_),save_predictions(_) ) ). %======================================================================== %= %= %= %======================================================================== delete_all_queries :- query_directory(Directory), atomic_concat(['rm -f ',Directory,'query_*'],Command), (shell(Command) -> true; true), retractall(query_is_similar(_,_)), retractall(query_md5(_,_)). empty_output_directory :- output_directory(Directory), atomic_concat(['rm -f ',Directory,'log.dat ', Directory,'factprobs_*.pl ', Directory,'predictions_*.pl'],Command), (shell(Command) -> true; true). %======================================================================== %= This predicate goes over all training and test examples, %= calls the inference method of ProbLog and stores the resulting %= BDDs %======================================================================== init_queries :- ( % go over all training examples current_predicate(user:example/3), user:example(ID,Query,Prob), format('~n~n training example ~q: ~q~n',[ID,Query]), flush_output(user), init_one_query(ID,Query), fail; %go to next training example true ), ( % go over all test examples current_predicate(user:test_example/3), user:test_example(ID,Query,Prob), format('~n~n test example ~q: ~q~n',[ID,Query]), flush_output(user), init_one_query(ID,Query), fail; % go to next test example true ). init_one_query(QueryID,Query) :- output_directory(Output_Directory), query_directory(Query_Directory), atomic_concat([Query_Directory,'query_',QueryID],Filename), atomic_concat([Output_Directory,'input.txt'],Filename2), atomic_concat([Output_Directory,'tmp_md5'],Filename3), ( file_exists(Filename) -> format('Reuse existing BDD ~q~n~n',[Filename]); ( init_method(Query,_Prob,Filename,Filename2,InitCall), once(call(InitCall)), delete_file(Filename2) ) ), ( check_duplicate_bdds(true) -> ( % calculate the md5sum of the bdd script file atomic_concat(['cat ',Filename,' | md5sum | sed "s/ .*$/\')./" | sed "s/^/md5(\'/"> ',Filename3],MD5Command), (shell(MD5Command,0) -> true; throw(error("Something wrong with calculating the MD5 value"))), open(Filename3, read, Handle), read(Handle,md5(Query_MD5)), close(Handle), delete_file(Filename3), % Does another query exists which already has this MD5? ( query_md5(OtherQueryID,Query_MD5) -> % yippie! we can save a lot of work ( assert(query_is_similar(QueryID,OtherQueryID)), format('~q is similar to ~q~2n', [QueryID,OtherQueryID]) ); assert(query_md5(QueryID,Query_MD5)) ) ); true ). %======================================================================== %= set all unknown fact probabilities to random values %= %= %======================================================================== initialize_fact_probabilities :- ( % go over all tunable facts tunable_fact(FactID,_), probability_initializer(FactID,Probability,Query), once(call(Query)), set_fact_probability(FactID,Probability), fail; % go to next tunable fact true ). random_probability(_FactID,Probability) :- % use probs around 0.5 to not confuse k-best search random(Random), Probability is 0.5+(Random-0.5)/100. %======================================================================== %= updates all values of query_probability/2 and query_gradient/3 %= should be called always before these predicates are accessed %= if the old values are still valid, nothing happens %======================================================================== update_values :- update_values(all). update_values(_) :- values_correct, !. update_values(What_To_Update) :- \+ values_correct, %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % delete old values %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% once(retractall(query_probability_intern(_,_))), once(retractall(query_gradient_intern(_,_,_))), output_directory(Directory), atomic_concat(Directory,'input.txt',Input_Filename), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start write current probabilities to file %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% open(Input_Filename,'write',Handle), ( % go over all probabilistic fact 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 ), close(Handle), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % stop write current probabilities to file %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% !, %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start update values for all test examples %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( What_To_Update = all -> ( % go over all training examples current_predicate(user:test_example/3), user:test_example(QueryID,_Query,_QueryProb), once(call_bdd_tool(QueryID,'+',all)), fail; % go to next training example true ); true ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % stop update values for all test examples %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% !, %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start update values for all training examples %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( % go over all training examples current_predicate(user:example/3), user:example(QueryID,_Query,_QueryProb), once(call_bdd_tool(QueryID,'.',What_To_Update)), fail; % go to next training example true ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % stop update values for all training examples %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% !, nl, delete_file(Input_Filename), assert(values_correct). %======================================================================== %= %= %= %======================================================================== call_bdd_tool(QueryID,Symbol,What_To_Update) :- output_directory(Output_Directory), query_directory(Query_Directory), ( query_is_similar(QueryID,_) -> % we don't have to evaluate the BDD write('#'); ( sigmoid_slope(Slope), problog_dir(PD), (What_To_Update=all -> Method='g' ; Method='l'), atomic_concat([PD, '/ProblogBDD -i "', Output_Directory, 'input.txt', '" -l "', Query_Directory, 'query_', QueryID, '" -m ',Method,' -id ', QueryID, ' -sl ',Slope, ' > "', Output_Directory, 'values.pl"'],Command), shell(Command,Error), atomic_concat([' cat "', Output_Directory, 'values.pl" >> ~/all_results'],Command2), shell(Command2), ( Error = 2 -> throw(error('SimpleCUDD has been interrupted.')); true ), ( Error \= 0 -> throw(bdd_error(QueryID,Error)); true ), atomic_concat([Output_Directory,'values.pl'],Values_Filename), ( file_exists(Values_Filename) -> ( ( once(my_load(Values_Filename)) -> true; ( format(user_error,'ERROR: Tried to read the file ~q but my_load/1 fails.~n~q.~2n',[Values_Filename,call_bdd_tool(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,call_bdd_tool(QueryID,Symbol,What_To_Update)]), throw(error(output_file_does_not_exist)) ) ) ), delete_file(Values_Filename), write(Symbol) ) ), flush_output(user). %======================================================================== %= %= %= %======================================================================== my_load(File) :- see(File), read(X), my_load_intern(X), seen. my_load_intern(end_of_file) :- !. my_load_intern(query_probability(QueryID,Prob)) :- !, assert(query_probability_intern(QueryID,Prob)), read(X2), my_load_intern(X2). my_load_intern(query_gradient(QueryID,XFactID,Value)) :- !, atomic_concat(x,StringFactID,XFactID), atom_number(StringFactID,FactID), assert(query_gradient_intern(QueryID,FactID,Value)), read(X2), my_load_intern(X2). my_load_intern(X) :- format(user_error,'Unknown atom ~q in results file.~n',[X]), read(X2), my_load_intern(X2). %======================================================================== %= %= %= %======================================================================== query_probability(QueryID,Prob) :- ( query_probability_intern(QueryID,Prob) -> true; ( query_is_similar(QueryID,OtherQueryID), query_probability_intern(OtherQueryID,Prob) ) ). query_gradient(QueryID,Fact,Value) :- ( query_gradient_intern(QueryID,Fact,Value) -> true; ( query_is_similar(QueryID,OtherQueryID), query_gradient_intern(OtherQueryID,Fact,Value) ) ). %======================================================================== %= %= %= %======================================================================== ground_truth_difference :- findall(Diff,(tunable_fact(FactID,GroundTruth), \+ var(GroundTruth), get_fact_probability(FactID,Prob), Diff is abs(GroundTruth-Prob)),AllDiffs), % if no ground truth was specified for facts % set everything to zero ( 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/3) -> ( update_values(probabilities), findall(SquaredError, (user:example(QueryID,_Query,QueryProb), query_probability(QueryID,CurrentProb), SquaredError is (CurrentProb-QueryProb)**2), AllSquaredErrors), length(AllSquaredErrors,Length), sum_list(AllSquaredErrors,SumAllSquaredErrors), MSE is SumAllSquaredErrors/Length ); true ), retractall(values_correct). % calculate the mse of the training data mse_trainingset :- ( current_predicate(user:example/3) -> ( update_values, findall(SquaredError, (user:example(QueryID,_Query,QueryProb), query_probability(QueryID,CurrentProb), SquaredError is (CurrentProb-QueryProb)**2), AllSquaredErrors), length(AllSquaredErrors,Length), sum_list(AllSquaredErrors,SumAllSquaredErrors), min_list(AllSquaredErrors,MinError), max_list(AllSquaredErrors,MaxError), MSE is SumAllSquaredErrors/Length, logger_set_variable(mse_trainingset,MSE), logger_set_variable(mse_min_trainingset,MinError), logger_set_variable(mse_max_trainingset,MaxError) ); true ). mse_testset :- ( current_predicate(user:test_example/3) -> ( update_values, findall(SquaredError, (user:test_example(QueryID,_Query,QueryProb), query_probability(QueryID,CurrentProb), SquaredError is (CurrentProb-QueryProb)**2), 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) ); 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) :- sigmoid_slope(Slope), Sig is 1/(1+exp(-T*Slope)). inv_sigmoid(T,InvSig) :- sigmoid_slope(Slope), InvSig is -log(1/T-1)/Slope. %======================================================================== %= this functions truncates probabilities too close to 1.0 or 0.0 %= the reason is, applying the inverse sigmoid function would yield +/- inf %= for such extreme values %= %= +Float, -Float %======================================================================== secure_probability(Prob,Prob_Secure) :- TMP is max(0.000000001,Prob), Prob_Secure is min(0.999999999,TMP). %======================================================================== %= Perform one iteration of gradient descent %= %= assumes that everything is initialized, if the current values %= of query_probability/2 and query_gradient/3 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,_), get_fact_probability(FactID,OldProbability), atomic_concat(['old_prob_',FactID],Key), bb_put(Key,OldProbability), fail; % go to next tunable fact true ). forget_old_values :- ( % go over all tunable facts tunable_fact(FactID,_), 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,_), 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 secure_probability(NewProbability,NewProbabilityS), set_fact_probability(FactID,NewProbabilityS), fail; % go to next tunable fact true ), retractall(values_correct). simulate :- L = [0.6,1.0,2.0,3.0,10,50,100,200,300], findall((X,Y),(member(X,L),line_search_evaluate_point(X,Y)),List), write(List),nl. gradient_descent :- save_old_probabilities, update_values, %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start set gradient to zero %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( % go over all tunable facts tunable_fact(FactID,_), atomic_concat(['grad_',FactID],Key), bb_put(Key,0.0), fail; % go to next tunable fact true ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % stop gradient to zero %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% !, %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start calculate gradient %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% alpha(Alpha), example_count(ExampleCount), ( % go over all training examples current_predicate(user:example/3), user:example(QueryID,_Query,QueryProb), query_probability(QueryID,BDDProb), ( QueryProb=:=0.0 -> Y2=Alpha; Y2=1.0 ), Y is Y2*2/ExampleCount * (BDDProb-QueryProb), ( % go over all tunable facts tunable_fact(FactID,_), 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,GradValue), bb_get(Key,OldValue), NewValue is OldValue + Y*GradValue, bb_put(Key,NewValue), fail; % go to next fact true ), 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)),GradientValues), sum_list(GradientValues,GradSum), max_list(GradientValues,GradMax), min_list(GradientValues,GradMin), length(GradientValues,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 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start add gradient to current probabilities %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( line_search(false) -> learning_rate(LearningRate); lineSearch(LearningRate,_) ), format('learning rate = ~12f~n',[LearningRate]), add_gradient(LearningRate), logger_set_variable(learning_rate,LearningRate), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % stop add gradient to current probabilities %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% !, forget_old_values. %======================================================================== %= %= %======================================================================== line_search_evaluate_point(Learning_Rate,MSE) :- add_gradient(Learning_Rate), mse_trainingset_only_for_linesearch(MSE). lineSearch(Final_X,Final_Value) :- % Get Parameters for line search line_search_tolerance(Tol), line_search_tau(Tau), line_search_interval(A,B), format(' Running line search in interval (~5f,~5f)~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), write(lineSearch(Iteration,Ak,Fl,Bk,Fr,Left,FLeft,Right,FRight)),nl, ( % 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, ActAcc is BkNew -AkNew, 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? ActAcc < Acc, % apperantly it is, so get me out of here and % cut away the choice point from repeat ! ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%% END BACK TRACKING %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % clean up the blackboard mess bb_delete(line_search_iteration,_), bb_delete(line_search_a,_), bb_delete(line_search_b,_), bb_delete(line_search_left,_), bb_delete(line_search_right,_), bb_delete(line_search_value_a,_), bb_delete(line_search_value_b,_), bb_delete(line_search_value_left,_), bb_delete(line_search_value_right,_), % it doesn't harm to check also the value in the middle % of the current search interval Middle is (AkNew + BkNew) / 2.0, line_search_evaluate_point(Middle,Value_Middle), % return the optimal value my_5_min(Value_Middle,FlNew,FrNew,FLeftNew,FRightNew, Middle,AkNew,BkNew,LeftNew,RightNew, Optimal_Value,Optimal_X), line_search_postcheck(Optimal_Value,Optimal_X,Final_Value,Final_X). line_search_postcheck(V,X,V,X) :- X>0, !. line_search_postcheck(V,X,V,X) :- line_search_never_stop(false), !. line_search_postcheck(_,_, LLH, FinalPosition) :- line_search_tolerance(Tolerance), 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, set_linesearch_weights_calc_llh(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 %= %======================================================================== global_initialize :- set_learning_flag(output_directory,'./output'), set_learning_flag(query_directory,'./queries'), set_learning_flag(log_frequency,5), set_learning_flag(rebuild_bdds,false), set_learning_flag(rebuild_bdds_it,1), set_learning_flag(reuse_initialized_bdds,false), set_learning_flag(learning_rate,examples), set_learning_flag(check_duplicate_bdds,true), set_learning_flag(probability_initializer,(FactID,P,random_probability(FactID,P))), set_learning_flag(alpha,1.0), set_learning_flag(sigmoid_slope,1.0), % 1.0 gives standard sigmoid set_learning_flag(init_method,(Query,Probability,BDDFile,ProbFile, problog_kbest_save(Query,100,Probability,_Status,BDDFile,ProbFile))), set_learning_flag(line_search,false), set_learning_flag(line_search_never_stop,true), set_learning_flag(line_search_tau,0.618033988749895), set_learning_flag(line_search_tolerance,0.05), set_learning_flag(line_search_interval,(0,100)), 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). %======================================================================== %= %= %======================================================================== :- initialization(global_initialize).