%%% -*- 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 % % ProbLog was developed at Katholieke Universiteit Leuven % % Copyright 2009 % Angelika Kimmig, Vitor Santos Costa, Bernd Gutmann % % 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, learning_flag/2, learning_flags/0, problog_help/0, set_problog_flag/2, problog_flag/2, problog_flags/0, auto_alpha/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('problog_learning/logger'). :- use_module('problog_learning/flags_learning'). :- use_module(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_md5/3. %======================================================================== %= %= %= %======================================================================== my_format(Level,String,Arguments) :- learning_flag(verbosity_level,V_Level), ( V_Level >= Level -> (format(String,Arguments),flush_output(user)); true ). %======================================================================== %= store the facts with the learned probabilities to a file %= if F is a variable, a filename based on the current iteration is used %= %======================================================================== save_model:- current_iteration(Iteration), learning_flag(output_directory,Directory), atomic_concat([Directory,'factprobs_',Iteration,'.pl'],F), export_facts(F). %======================================================================== %= store the current succes probabilities for training and test examples %= %======================================================================== save_predictions:- current_iteration(Iteration), learning_flag(output_directory,Directory), atomic_concat([Directory,'predictions_',Iteration,'.pl'],F), open(F,'append',Handle), format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",[]), format(Handle,"% Iteration, train/test, QueryID, Query, GroundTruth, Prediction %\n",[]), format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",[]), !, %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start save prediction test examples %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( % go over all test examples current_predicate(user:test_example/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 ), !, %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % stop save prediction test examples %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start save prediction training examples %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( % 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 ), !, %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % stop save prediction training examples %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% format(Handle,'~3n',[]), close(Handle). %======================================================================== %= find out whether some example IDs are used more than once %= if so, complain and stop %= %======================================================================== check_examples :- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Check example IDs %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( (current_predicate(user:example/3),user:example(ID,_,_), \+ atomic(ID)) -> ( format(user_error,'The example id of training example ~q ',[ID]), format(user_error,'is not atomic (e.g foo42, 23, bar, ...).~n',[]), throw(error(examples)) ); true ), ( (current_predicate(user:test_example/3),user:test_example(ID,_,_), \+ atomic(ID)) -> ( format(user_error,'The example id of test example ~q ',[ID]), format(user_error,'is not atomic (e.g foo42, 23, bar, ...).~n',[]), throw(error(examples)) ); true ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Check example probabilities %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( (current_predicate(user:example/3),user:example(ID,_,P), (\+ number(P); P>1 ; P<0)) -> ( format(user_error,'The training example ~q does not have a valid probaility value (~q).~n',[ID,P]), throw(error(examples)) ); true ), ( (current_predicate(user:test_example/3),user:test_example(ID,_,P), (\+ number(P); P>1 ; P<0)) -> ( format(user_error,'The test example ~q does not have a valid probaility value (~q).~n',[ID,P]), throw(error(examples)) ); true ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Check that no example ID is repeated, % and if it is repeated make sure the query is the same %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( ( ( current_predicate(user:example/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) :- do_learning(Iterations,-1). do_learning(Iterations,Epsilon) :- current_predicate(user:example/3), !, integer(Iterations), number(Epsilon), Iterations>0, do_learning_intern(Iterations,Epsilon). do_learning(_,_) :- format(user_error,'~n~nWarning: 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, assert(current_iteration(NextIteration)), EndIteration is CurrentIteration+Iterations-1, my_format(1,'~nIteration ~d of ~d~n',[CurrentIteration,EndIteration]), logger_set_variable(iteration,CurrentIteration), logger_start_timer(duration), mse_testset, once(ground_truth_difference), gradient_descent, learning_flag(log_frequency,Log_Frequency), ( ( Log_Frequency>0, 0 =:= CurrentIteration mod Log_Frequency) -> ( once(save_predictions), once(save_model) ); true ), update_values, ( last_mse(Last_MSE) -> ( retractall(last_mse(_)), logger_get_variable(mse_trainingset,Current_MSE), 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 ) ), ( (learning_flag(rebuild_bdds,BDDFreq),BDDFreq>0,0 =:= CurrentIteration mod BDDFreq) -> ( retractall(values_correct), once(delete_all_queries), once(init_queries) ); true ), !, logger_stop_timer(duration), logger_write_data, RemainingIterations is Iterations-1, ( MSE_Diff>Epsilon -> do_learning_intern(RemainingIterations,Epsilon); true ). %======================================================================== %= find proofs and build bdds for all training and test examples %= %= %======================================================================== init_learning :- learning_initialized, !. init_learning :- check_examples, logger_write_header, my_format(1,'Initializing everything~n',[]), empty_output_directory, %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Delete the BDDs from the previous run if they should % not be reused %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( ( learning_flag(reuse_initialized_bdds,true), learning_flag(rebuild_bdds,0) ) -> true; delete_all_queries ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start count test examples %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 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; % go to next text example true ), bb_delete(test_examples,TestExampleCount), my_format(3,'~q test examples~n',[TestExampleCount]), !, %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % stop count test examples %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start count training examples %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% bb_put(training_examples,0), ( % go over all training examples current_predicate(user:example/3), user:example(_,_,_), bb_get(training_examples, OldCounter), NewCounter is OldCounter+1, bb_put(training_examples,NewCounter), fail; %go to next training example true ), bb_delete(training_examples,TrainingExampleCount), assert(example_count(TrainingExampleCount)), my_format(3,'~q training examples~n',[TrainingExampleCount]), !, %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % stop count training examples %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % set learning rate and alpha %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( learning_flag(learning_rate,examples) -> set_learning_flag(learning_rate,TrainingExampleCount); true ), ( learning_flag(alpha,auto) -> auto_alpha; true ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % build BDD script for every example %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% once(initialize_fact_probabilities), once(init_queries), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % done %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% assert(current_iteration(0)), assert(learning_initialized), my_format(1,'~n',[]). %======================================================================== %= %= %= %======================================================================== delete_all_queries :- remove_queries, retractall(query_is_similar(_,_)), retractall(query_md5(_,_,_)). remove_queries :- learning_flag(query_directory,Directory), user:example(ID,_,_), atomic_concat([Directory,'query_',ID],File), delete_file(File), fail. remove_queries. empty_output_directory :- learning_flag(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 :- my_format(2,'Build BDDs for examples~n',[]), ( % go over all test examples current_predicate(user:test_example/3), user:test_example(ID,Query,Prob), my_format(3,' test example ~q: ~q~n',[ID,Query]), flush_output(user), init_one_query(ID,Query,test), fail; % go to next test example true ), ( % go over all training examples current_predicate(user:example/3), user:example(ID,Query,Prob), statistics(runtime,[_,T]), my_format(3,' training example ~q: ~q after ~q msec~n',[ID,Query,T]), % my_format(3,' training example ~q: ~q~n',[ID,Query]), flush_output(user), init_one_query(ID,Query,training), fail; %go to next training example true ). init_one_query(QueryID,Query,Type) :- learning_flag(output_directory,Output_Directory), learning_flag(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), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % if BDD file does not exist, call ProbLog %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( file_exists(Filename) -> my_format(3,' Reuse existing BDD ~q~n~n',[Filename]); ( learning_flag(init_method,(Query,_Prob,Filename,Filename2,InitCall)), once(call(InitCall)), delete_file(Filename2) ) ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % check wether this BDD is similar to a previous one of the same type %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( learning_flag(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,Type) -> % yippie! we can save a lot of work ( assert(query_is_similar(QueryID,OtherQueryID)), my_format(3, '~q is similar to ~q~2n', [QueryID,OtherQueryID]) ); assert(query_md5(QueryID,Query_MD5,Type)) ) ); true ). %======================================================================== %= set all unknown fact probabilities to random values %= %= %======================================================================== initialize_fact_probabilities :- ( % go over all tunable facts tunable_fact(FactID,_), learning_flag(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 :- values_correct, !. update_values :- \+ values_correct, %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % delete old values %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% once(retractall(query_probability_intern(_,_))), once(retractall(query_gradient_intern(_,_,_))), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start write current probabilities to file %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% learning_flag(output_directory,Directory), atomic_concat(Directory,'input.txt',Input_Filename), ( file_exists(Input_Filename) -> delete_file(Input_Filename); true ), 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 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% assert(values_correct). %======================================================================== %= %= %= %======================================================================== update_query_cleanup(QueryID) :- ( (query_is_similar(QueryID,_) ; query_is_similar(_,QueryID)) -> % either this query is similar to another or vice versa, % therefore we don't delete anything true; once(retractall(query_gradient_intern(QueryID,_,_))) ). update_query(QueryID,Symbol,What_To_Update) :- learning_flag(output_directory,Output_Directory), learning_flag(query_directory,Query_Directory), ( query_is_similar(QueryID,_) -> % we don't have to evaluate the BDD my_format(4,'#',[]); ( learning_flag(sigmoid_slope,Slope), problog_dir(PD), ((What_To_Update=all;query_is_similar(_,QueryID)) -> 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), ( 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,QueryID)) -> true; ( format(user_error,'ERROR: Tried to read the file ~q but my_load/1 fails.~n~q.~2n',[Values_Filename,update_query(QueryID,Symbol,What_To_Update)]), throw(error(my_load_fails)) ) ); ( format(user_error,'ERROR: Tried to read the file ~q but it does not exist.~n~q.~2n',[Values_Filename,update_query(QueryID,Symbol,What_To_Update)]), throw(error(output_file_does_not_exist)) ) ) ), delete_file(Values_Filename), my_format(4,'~w',[Symbol]) ) ), flush_output(user). %======================================================================== %= This predicate reads probability and gradient values from the file %= the gradient ID is a mere check to uncover hidden bugs %= +Filename +QueryID -QueryProbability %======================================================================== my_load(File,QueryID) :- open(File,'read',Handle), read(Handle,Atom), once(my_load_intern(Atom,Handle,QueryID)), !, close(Handle). my_load(File,QueryID) :- format(user_error,'Error at ~q.~2n',[my_load(File,QueryID)]), throw(error(my_load(File,QueryID))). my_load_intern(end_of_file,_,_) :- !. my_load_intern(query_probability(QueryID,Prob),Handle,QueryID) :- !, assert(query_probability_intern(QueryID,Prob)), read(Handle,X), my_load_intern(X,Handle,QueryID). my_load_intern(query_gradient(QueryID,XFactID,Value),Handle,QueryID) :- !, atomic_concat(x,StringFactID,XFactID), atom_number(StringFactID,FactID), assert(query_gradient_intern(QueryID,FactID,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,X), my_load_intern(X,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,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, findall(SquaredError, (user:example(QueryID,_Query,QueryProb), once(update_query(QueryID,'.',probability)), query_probability(QueryID,CurrentProb), once(update_query_cleanup(QueryID)), SquaredError is (CurrentProb-QueryProb)**2), AllSquaredErrors), length(AllSquaredErrors,Length), sum_list(AllSquaredErrors,SumAllSquaredErrors), MSE is SumAllSquaredErrors/Length, my_format(3,' (~8f)~n',[MSE]) ); true ), retractall(values_correct). % calculate the mse of the test data mse_testset :- ( current_predicate(user:test_example/3) -> ( my_format(2,'MSE_Test ',[]), update_values, findall(SquaredError, (user:test_example(QueryID,_Query,QueryProb), once(update_query(QueryID,'+',probability)), query_probability(QueryID,CurrentProb), once(update_query_cleanup(QueryID)), 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), my_format(2,' (~8f)~n',[MSE]) ); true ). %======================================================================== %= Calculates the sigmoid function respectivly the inverse of it %= warning: applying inv_sigmoid to 0.0 or 1.0 will yield +/-inf %= %= +Float, -Float %======================================================================== sigmoid(T,Sig) :- learning_flag(sigmoid_slope,Slope), Sig is 1/(1+exp(-T*Slope)). inv_sigmoid(T,InvSig) :- learning_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/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_probabilities :- ( % 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 Prob_Secure is min(0.999999999,max(0.000000001,NewProbability)), set_fact_probability(FactID,Prob_Secure), fail; % go to next tunable fact true ), !, retractall(values_correct). gradient_descent :- my_format(2,'Gradient ',[]), 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 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% bb_put(mse_train_sum, 0.0), bb_put(mse_train_min, 0.0), bb_put(mse_train_max, 0.0), learning_flag(alpha,Alpha), logger_set_variable(alpha,Alpha), example_count(Example_Count), ( % go over all training examples current_predicate(user:example/3), user:example(QueryID,_Query,QueryProb), once(update_query(QueryID,'.',all)), query_probability(QueryID,BDDProb), ( QueryProb=:=0.0 -> Y2=Alpha; Y2=1.0 ), Y is Y2*2/Example_Count * (BDDProb-QueryProb), % first do the calculations for the MSE on training set Squared_Error is (BDDProb-QueryProb)**2, bb_get(mse_train_sum,Old_MSE_Train_Sum), bb_get(mse_train_min,Old_MSE_Train_Min), bb_get(mse_train_max,Old_MSE_Train_Max), New_MSE_Train_Sum is Old_MSE_Train_Sum+Squared_Error, New_MSE_Train_Min is min(Old_MSE_Train_Min,Squared_Error), New_MSE_Train_Max is max(Old_MSE_Train_Max,Squared_Error), bb_put(mse_train_sum,New_MSE_Train_Sum), bb_put(mse_train_min,New_MSE_Train_Min), bb_put(mse_train_max,New_MSE_Train_Max), ( % go over all tunable facts tunable_fact(FactID,_), 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 ), once(update_query_cleanup(QueryID)), fail; % go to next training example true ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % stop calculate gradient %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% !, %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start statistics on gradient %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% findall(V, ( tunable_fact(FactID,_), atomic_concat(['grad_',FactID],Key), bb_get(Key,V) ),Gradient_Values), sum_list(Gradient_Values,GradSum), max_list(Gradient_Values,GradMax), min_list(Gradient_Values,GradMin), length(Gradient_Values,GradLength), GradMean is GradSum/GradLength, logger_set_variable(gradient_mean,GradMean), logger_set_variable(gradient_min,GradMin), logger_set_variable(gradient_max,GradMax), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % stop statistics on gradient %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% bb_delete(mse_train_sum,MSE_Train_Sum), bb_delete(mse_train_min,MSE_Train_Min), bb_delete(mse_train_max,MSE_Train_Max), MSE is MSE_Train_Sum/Example_Count, logger_set_variable(mse_trainingset,MSE), logger_set_variable(mse_min_trainingset,MSE_Train_Min), logger_set_variable(mse_max_trainingset,MSE_Train_Max), my_format(2,'~n',[]), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start add gradient to current probabilities %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( learning_flag(line_search,false) -> learning_flag(learning_rate,LearningRate); lineSearch(LearningRate,_) ), my_format(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), my_format(2,'Line search (h=~8f) ',[Learning_Rate]), mse_trainingset_only_for_linesearch(MSE). lineSearch(Final_X,Final_Value) :- % Get Parameters for line search learning_flag(line_search_tolerance,Tol), learning_flag(line_search_tau,Tau), learning_flag(line_search_interval,(A,B)), my_format(3,'Line search in interval (~4f,~4f)~n',[A,B]), % init values Acc is Tol * (B-A), InitRight is A + Tau*(B-A), InitLeft is A + B - InitRight, line_search_evaluate_point(A,Value_A), line_search_evaluate_point(B,Value_B), line_search_evaluate_point(InitRight,Value_InitRight), line_search_evaluate_point(InitLeft,Value_InitLeft), bb_put(line_search_a,A), bb_put(line_search_b,B), bb_put(line_search_left,InitLeft), bb_put(line_search_right,InitRight), bb_put(line_search_value_a,Value_A), bb_put(line_search_value_b,Value_B), bb_put(line_search_value_left,Value_InitLeft), bb_put(line_search_value_right,Value_InitRight), bb_put(line_search_iteration,1), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%% BEGIN BACK TRACKING %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( repeat, bb_get(line_search_iteration,Iteration), bb_get(line_search_a,Ak), bb_get(line_search_b,Bk), bb_get(line_search_left,Left), bb_get(line_search_right,Right), bb_get(line_search_value_a,Fl), bb_get(line_search_value_b,Fr), bb_get(line_search_value_left,FLeft), bb_get(line_search_value_right,FRight), ( % check for infinity, if there is, go to the left ( FLeft >= FRight, \+ FLeft = (+inf), \+ FRight = (+inf) ) -> ( AkNew=Left, FlNew=FLeft, LeftNew=Right, FLeftNew=FRight, RightNew is AkNew + Bk - LeftNew, line_search_evaluate_point(RightNew,FRightNew), BkNew=Bk, FrNew=Fr ); ( BkNew=Right, FrNew=FRight, RightNew=Left, FRightNew=FLeft, LeftNew is Ak + BkNew - RightNew, line_search_evaluate_point(LeftNew,FLeftNew), AkNew=Ak, FlNew=Fl ) ), Next_Iteration is Iteration + 1, bb_put(line_search_iteration,Next_Iteration), bb_put(line_search_a,AkNew), bb_put(line_search_b,BkNew), bb_put(line_search_left,LeftNew), bb_put(line_search_right,RightNew), bb_put(line_search_value_a,FlNew), bb_put(line_search_value_b,FrNew), bb_put(line_search_value_left,FLeftNew), bb_put(line_search_value_right,FRightNew), % is the search interval smaller than the tolerance level? BkNew-AkNew0, !. line_search_postcheck(V,X,V,X) :- learning_flag(line_search_never_stop,false), !. line_search_postcheck(_,_, LLH, FinalPosition) :- learning_flag(line_search_tolerance,Tolerance), learning_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, 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) ). %======================================================================== %= set the alpha parameter to the value %= # positive training examples / # negative training examples %= %= training example is positive if P(e)=1 %= training example is negative if P(e)=0 %= %= if there are training example with 00, !, set_learning_flag(alpha,1.0). auto_alpha :- findall(1,(user:example(_,_,P),P=:=1.0),Pos), findall(0,(user:example(_,_,P),P=:=0.0),Neg), length(Pos,NP), length(Neg,NN), Alpha is NP/NN, set_learning_flag(alpha,Alpha). %======================================================================== %= initialize the logger module and set the flags for learning %= don't change anything here! use set_learning_flag/2 instead %======================================================================== global_initialize :- set_learning_flag(output_directory,'./output'), set_learning_flag(query_directory,'./queries'), set_learning_flag(log_frequency,1), set_learning_flag(rebuild_bdds,0), 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,auto), 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,0.618033988749), set_learning_flag(line_search_tolerance,0.05), set_learning_flag(line_search_interval,(0,100)), set_learning_flag(verbosity_level,5), logger_define_variable(iteration, int), logger_define_variable(duration,time), logger_define_variable(mse_trainingset,float), logger_define_variable(mse_min_trainingset,float), logger_define_variable(mse_max_trainingset,float), logger_define_variable(mse_testset,float), logger_define_variable(mse_min_testset,float), logger_define_variable(mse_max_testset,float), logger_define_variable(gradient_mean,float), logger_define_variable(gradient_min,float), logger_define_variable(gradient_max,float), logger_define_variable(ground_truth_diff,float), logger_define_variable(ground_truth_mindiff,float), logger_define_variable(ground_truth_maxdiff,float), logger_define_variable(learning_rate,float), logger_define_variable(alpha,float). %======================================================================== %= %= %======================================================================== :- initialization(global_initialize).