%%% -*- 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. The intent is that the Copyright Holder maintains some % artistic control over the development of that Package while still % keeping the Package available as open source and free software. % % You are always permitted to make arrangements wholly outside of this % license directly with the Copyright Holder of a given Package. If the % terms of this license do not permit the full use that you propose to % make of the Package, you should contact the Copyright Holder and seek % a different licensing arrangement. Definitions % % "Copyright Holder" means the individual(s) or organization(s) named in % the copyright notice for the entire Package. % % "Contributor" means any party that has contributed code or other % material to the Package, in accordance with the Copyright Holder's % procedures. % % "You" and "your" means any person who would like to copy, distribute, % or modify the Package. % % "Package" means the collection of files distributed by the Copyright % Holder, and derivatives of that collection and/or of those files. A % given Package may consist of either the Standard Version, or a % Modified Version. % % "Distribute" means providing a copy of the Package or making it % accessible to anyone else, or in the case of a company or % organization, to others outside of your company or organization. % % "Distributor Fee" means any fee that you charge for Distributing this % Package or providing support for this Package to another party. It % does not mean licensing fees. % % "Standard Version" refers to the Package if it has not been modified, % or has been modified only in ways explicitly requested by the % Copyright Holder. % % "Modified Version" means the Package, if it has been changed, and such % changes were not explicitly requested by the Copyright Holder. % % "Original License" means this Artistic License as Distributed with the % Standard Version of the Package, in its current version or as it may % be modified by The Perl Foundation in the future. % % "Source" form means the source code, documentation source, and % configuration files for the Package. % % "Compiled" form means the compiled bytecode, object code, binary, or % any other form resulting from mechanical transformation or translation % of the Source form. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % Permission for Use and Modification Without Distribution % % (1) You are permitted to use the Standard Version and create and use % Modified Versions for any purpose without restriction, provided that % you do not Distribute the Modified Version. % % Permissions for Redistribution of the Standard Version % % (2) You may Distribute verbatim copies of the Source form of the % Standard Version of this Package in any medium without restriction, % either gratis or for a Distributor Fee, provided that you duplicate % all of the original copyright notices and associated disclaimers. At % your discretion, such verbatim copies may or may not include a % Compiled form of the Package. % % (3) You may apply any bug fixes, portability changes, and other % modifications made available from the Copyright Holder. The resulting % Package will still be considered the Standard Version, and as such % will be subject to the Original License. % % Distribution of Modified Versions of the Package as Source % % (4) You may Distribute your Modified Version as Source (either gratis % or for a Distributor Fee, and with or without a Compiled form of the % Modified Version) provided that you clearly document how it differs % from the Standard Version, including, but not limited to, documenting % any non-standard features, executables, or modules, and provided that % you do at least ONE of the following: % % (a) make the Modified Version available to the Copyright Holder of the % Standard Version, under the Original License, so that the Copyright % Holder may include your modifications in the Standard Version. (b) % ensure that installation of your Modified Version does not prevent the % user installing or running the Standard Version. In addition, the % modified Version must bear a name that is different from the name of % the Standard Version. (c) allow anyone who receives a copy of the % Modified Version to make the Source form of the Modified Version % available to others under (i) the Original License or (ii) a license % that permits the licensee to freely copy, modify and redistribute the % Modified Version using the same licensing terms that apply to the copy % that the licensee received, and requires that the Source form of the % Modified Version, and of any works derived from it, be made freely % available in that license fees are prohibited but Distributor Fees are % allowed. % % Distribution of Compiled Forms of the Standard Version or % Modified Versions without the Source % % (5) You may Distribute Compiled forms of the Standard Version without % the Source, provided that you include complete instructions on how to % get the Source of the Standard Version. Such instructions must be % valid at the time of your distribution. If these instructions, at any % time while you are carrying out such distribution, become invalid, you % must provide new instructions on demand or cease further % distribution. If you provide valid instructions or cease distribution % within thirty days after you become aware that the instructions are % invalid, then you do not forfeit any of your rights under this % license. % % (6) You may Distribute a Modified Version in Compiled form without the % Source, provided that you comply with Section 4 with respect to the % Source of the Modified Version. % % Aggregating or Linking the Package % % (7) You may aggregate the Package (either the Standard Version or % Modified Version) with other packages and Distribute the resulting % aggregation provided that you do not charge a licensing fee for the % Package. Distributor Fees are permitted, and licensing fees for other % components in the aggregation are permitted. The terms of this license % apply to the use and Distribution of the Standard or Modified Versions % as included in the aggregation. % % (8) You are permitted to link Modified and Standard Versions with % other works, to embed the Package in a larger work of your own, or to % build stand-alone binary or bytecode versions of applications that % include the Package, and Distribute the result without restriction, % provided the result does not expose a direct interface to the Package. % % Items That are Not Considered Part of a Modified Version % % (9) Works (including, but not limited to, modules and scripts) that % merely extend or make use of the Package, do not, by themselves, cause % the Package to be a Modified Version. In addition, such works are not % considered parts of the Package itself, and are not subject to the % terms of this license. % % General Provisions % % (10) Any use, modification, and distribution of the Standard or % Modified Versions is governed by this Artistic License. By using, % modifying or distributing the Package, you accept this license. Do not % use, modify, or distribute the Package, if you do not accept this % license. % % (11) If your Modified Version has been derived from a Modified Version % made by someone other than you, you are nevertheless required to % ensure that your Modified Version complies with the requirements of % this license. % % (12) This license does not grant you the right to use any trademark, % service mark, tradename, or logo of the Copyright Holder. % % (13) This license includes the non-exclusive, worldwide, % free-of-charge patent license to make, have made, use, offer to sell, % sell, import and otherwise transfer the Package with respect to any % patent claims licensable by the Copyright Holder that are necessarily % infringed by the Package. If you institute patent litigation % (including a cross-claim or counterclaim) against any party alleging % that the Package constitutes direct or contributory patent % infringement, then this Artistic License to you shall terminate on the % date that such litigation is filed. % % (14) Disclaimer of Warranty: THE PACKAGE IS PROVIDED BY THE COPYRIGHT % HOLDER AND CONTRIBUTORS "AS IS' AND WITHOUT ANY EXPRESS OR IMPLIED % WARRANTIES. THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A % PARTICULAR PURPOSE, OR NON-INFRINGEMENT ARE DISCLAIMED TO THE EXTENT % PERMITTED BY YOUR LOCAL LAW. UNLESS REQUIRED BY LAW, NO COPYRIGHT % HOLDER OR CONTRIBUTOR WILL BE LIABLE FOR ANY DIRECT, INDIRECT, % INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING IN ANY WAY OUT OF THE USE % OF THE PACKAGE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% :- module(learning,[do_learning/1, do_learning/2, set_problog_flag/2, problog_flag/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 :- use_module(problog). :- use_module('problog/logger'). :- use_module('problog/flags'). :- use_module('problog/os'). :- use_module('problog/print_learning'). :- use_module('problog/utils_lbdd'). :- use_module('problog/utils'). :- use_module('problog/tabling'). % used to indicate the state of the system :- dynamic(values_correct/0). :- dynamic(learning_initialized/0). :- dynamic(current_iteration/1). :- dynamic(example_count/1). :- dynamic(query_probability_intern/2). :- dynamic(query_gradient_intern/4). :- dynamic(last_mse/1). :- dynamic(query_is_similar/2). :- dynamic(query_md5/2). % used to identify queries which have identical proofs :- dynamic(query_is_similar/2). :- dynamic(query_md5/3). :- multifile(user:example/4). user:example(A,B,C,=) :- current_predicate(user:example/3), user:example(A,B,C). :- multifile(user:test_example/4). user:test_example(A,B,C,=) :- current_predicate(user:test_example/3), user:test_example(A,B,C). %======================================================================== %= store the facts with the learned probabilities to a file %======================================================================== save_model:- current_iteration(Iteration), create_factprobs_file_name(Iteration,Filename), export_facts(Filename). %======================================================================== %= find out whether some example IDs are used more than once %= if so, complain and stop %= %======================================================================== check_examples :- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Check example IDs %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( (user:example(ID,_,_,_), \+ atomic(ID)) -> ( format(user_error,'The example id of training example ~q ',[ID]), format(user_error,'is not atomic (e.g foo42, 23, bar, ...).~n',[]), throw(error(examples)) ); true ), ( (user:test_example(ID,_,_,_), \+ atomic(ID)) -> ( format(user_error,'The example id of test example ~q ',[ID]), format(user_error,'is not atomic (e.g foo42, 23, bar, ...).~n',[]), throw(error(examples)) ); true ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Check example probabilities %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( (user:example(ID,_,P,_), (\+ number(P); P>1 ; P<0)) -> ( format(user_error,'The training example ~q does not have a valid probability value (~q).~n',[ID,P]), throw(error(examples)) ); true ), ( (user:test_example(ID,_,P,_), (\+ number(P); P>1 ; P<0)) -> ( format(user_error,'The test example ~q does not have a valid probability value (~q).~n',[ID,P]), throw(error(examples)) ); true ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Check that no example ID is repeated, % and if it is repeated make sure the query is the same %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( ( ( user:example(ID,QueryA,_,_), user:example(ID,QueryB,_,_), QueryA \= QueryB ) ; ( user:test_example(ID,QueryA,_,_), user:test_example(ID,QueryB,_,_), QueryA \= QueryB ); ( user:example(ID,QueryA,_,_), user:test_example(ID,QueryB,_,_), QueryA \= QueryB ) ) -> ( format(user_error,'The example id ~q is used several times.~n',[ID]), throw(error(examples)) ); true ). %======================================================================== %= %======================================================================== reset_learning :- retractall(learning_initialized), retractall(values_correct), retractall(current_iteration(_)), retractall(example_count(_)), retractall(query_probability_intern(_,_)), retractall(query_gradient_intern(_,_,_)), retractall(last_mse(_)), retractall(query_is_similar(_,_)), retractall(query_md5(_,_,_)), set_problog_flag(alpha,auto), set_problog_flag(learning_rate,examples), logger_reset_all_variables. %======================================================================== %= initialize everything and perform Iterations times gradient descent %= can be called several times %= if it is called with an epsilon parameter, it stops when the change %= in the MSE is smaller than epsilon %======================================================================== do_learning(Iterations) :- do_learning(Iterations,-1). do_learning(Iterations,Epsilon) :- current_predicate(user:example/4), !, integer(Iterations), number(Epsilon), Iterations>0, do_learning_intern(Iterations,Epsilon). do_learning(_,_) :- format(user_error,'~n~Error: No training examples specified.~n~n',[]). do_learning_intern(0,_) :- !. do_learning_intern(Iterations,Epsilon) :- Iterations>0, init_learning, current_iteration(CurrentIteration), retractall(current_iteration(_)), NextIteration is CurrentIteration+1, assertz(current_iteration(NextIteration)), EndIteration is CurrentIteration+Iterations-1, format_learning(1,'~nIteration ~d of ~d~n',[CurrentIteration,EndIteration]), logger_set_variable(iteration,CurrentIteration), logger_start_timer(duration), mse_testset, ground_truth_difference, gradient_descent, problog_flag(log_frequency,Log_Frequency), ( ( Log_Frequency>0, 0 =:= CurrentIteration mod Log_Frequency) -> once(save_model); true ), update_values, ( last_mse(Last_MSE) -> ( retractall(last_mse(_)), logger_get_variable(mse_trainingset,Current_MSE), assertz(last_mse(Current_MSE)), !, MSE_Diff is abs(Last_MSE-Current_MSE) ); ( logger_get_variable(mse_trainingset,Current_MSE), assertz(last_mse(Current_MSE)), MSE_Diff is Epsilon+1 ) ), ( (problog_flag(rebuild_bdds,BDDFreq),BDDFreq>0,0 =:= CurrentIteration mod BDDFreq) -> ( retractall(values_correct), retractall(query_is_similar(_,_)), retractall(query_md5(_,_,_)), empty_bdd_directory, init_queries ); true ), !, logger_stop_timer(duration), logger_write_data, RemainingIterations is Iterations-1, ( MSE_Diff>Epsilon -> do_learning_intern(RemainingIterations,Epsilon); true ). %======================================================================== %= find proofs and build bdds for all training and test examples %= %= %======================================================================== init_learning :- learning_initialized, !. init_learning :- check_examples, % empty_output_directory, logger_write_header, format_learning(1,'Initializing everything~n',[]), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Delete the BDDs from the previous run if they should % not be reused %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( ( problog_flag(reuse_initialized_bdds,true), problog_flag(rebuild_bdds,0) ) -> true; empty_bdd_directory ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Check, if continuous facts are used. % if yes, switch to problog_exact % continuous facts are not supported yet. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% 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) ) ) ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 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. %======================================================================== %= This predicate goes over all training and test examples, %= calls the inference method of ProbLog and stores the resulting %= BDDs %======================================================================== init_queries :- format_learning(2,'Build BDDs for examples~n',[]), forall(user:test_example(ID,Query,_Prob,_),init_one_query(ID,Query,test)), forall(user:example(ID,Query,_Prob,_),init_one_query(ID,Query,training)). bdd_input_file(Filename) :- problog_flag(output_directory,Dir), concat_path_with_filename(Dir,'input.txt',Filename). init_one_query(QueryID,Query,Type) :- format_learning(3,' ~q example ~q: ~q~n',[Type,QueryID,Query]), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % if BDD file does not exist, call ProbLog %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( recorded(QueryID, _, _) -> format_learning(3,' Reuse existing BDD ~q~n~n',[QueryID]); ( problog_flag(libbdd_init_method,(Query,Bdd,Call)), Bdd = bdd(Tree, MapList), once(Call), rb_new(H0), maplist_to_hash(MapList, H0, Hash), tree_to_grad(Tree, Hash, [], Grad), recordz(QueryID,bdd(Grad,MapList),_) ) ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % check wether this BDD is similar to another BDD %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ( problog_flag(check_duplicate_bdds,true) -> true /* ignore this flag for now */ ; true ),!. %======================================================================== %= updates all values of query_probability/2 and query_gradient/4 %= should be called always before these predicates are accessed %= if the old values are still valid, nothing happens %======================================================================== update_values :- values_correct, !. update_values :- \+ values_correct, %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % delete old values %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% retractall(query_probability_intern(_,_)), retractall(query_gradient_intern(_,_,_,_)), assertz(values_correct). %======================================================================== %= %= %= %======================================================================== update_query_cleanup(QueryID) :- ( (query_is_similar(QueryID,_) ; query_is_similar(_,QueryID)) -> % either this query is similar to another or vice versa, % therefore we don't delete anything true; retractall(query_gradient_intern(QueryID,_,_,_)) ). update_query(QueryID,Symbol,What_To_Update) :- ( query_is_similar(QueryID,_) -> % we don't have to evaluate the BDD format_learning(4,'#',[]); ( problog_flag(sigmoid_slope,Slope), ((What_To_Update=all;query_is_similar(_,QueryID)) -> Method='g' ; Method='l'), gradient(QueryID, Method, Slope), format_learning(4,'~w',[Symbol]) ) ). bind_maplist([]). bind_maplist([Node-Theta|MapList]) :- get_prob(Node, ProbFact), inv_sigmoid(ProbFact, Theta), bind_maplist(MapList). %get_prob(Node, Prob) :- % query_probability(Node,Prob), !. get_prob(Node, Prob) :- get_fact_probability(Node,Prob). gradient(QueryID, l, Slope) :- /* query_probability(21,6.775948e-01). */ recorded(QueryID, bdd(Tree, MapList), _), bind_maplist(MapList), run_sp(Tree, Slope, 0.0, Prob), assert(query_probability_intern(QueryID,Prob)), fail. gradient(_QueryID, l, _). gradient(QueryID, g, Slope) :- /* query_gradient(17,x2,p,6.736196e-02). query_probability(17,1.173512e-01). */ recorded(QueryID, bdd(Tree, MapList), _), bind_maplist(MapList), member(I-_, MapList), run_grad(Tree, I, Slope, 0.0, Grad), % writeln(query_gradient_intern(QueryID,I,p,Grad)), assert(query_gradient_intern(QueryID,I,p,Grad)), fail. gradient(QueryID, g, Slope) :- gradient(QueryID, l, Slope). maplist_to_hash([], H0, H0). maplist_to_hash([I-V|MapList], H0, Hash) :- rb_insert(H0, V, I, H1), maplist_to_hash(MapList, H1, Hash). tree_to_grad([], _, Grad, Grad). tree_to_grad([Node|Tree], H, Grad0, Grad) :- node_to_gradient_node(Node, H, GNode), tree_to_grad(Tree, H, [GNode|Grad0], Grad). /* pp should never happen */ node_to_gradient_node(pp(P-G,X,L,R), H, gnodep(P,G,X,Id,PL,GL,PR,GR)) :- rb_lookup(X,Id,H), (L == 1 -> GL=0, PL=1 ; L == 0 -> GL = 0, PL=0 ; L = PL-GL), (R == 1 -> GR=0, PR=1 ; R == 0 -> GR = 0, PR=0 ; R = PR-GR). node_to_gradient_node(pn(P-G,X,L,R), H, gnoden(P,G,X,Id,PL,GL,PR,GR)) :- rb_lookup(X,Id,H), (L == 1 -> GL=0, PL=1 ; L == 0 -> GL = 0, PL=0 ; L = PL-GL), (R == 1 -> GR=0, PR=1 ; R == 0 -> GR = 0, PR=0 ; R = PR-GR). run_sp([], _, P0, P0). run_sp(gnodep(P,_G, X, _Id, PL, _GL, PR, _GR).Tree, Slope, _, PF) :- P is (PL / (1.0 + exp(-X * Slope)))+ (PR / (1.0 + exp(X * Slope))), run_sp(Tree, Slope, P, PF). run_sp(gnoden(P,_G, X, _Id, PL, _GL, PR, _GR).Tree, Slope, _, PF) :- P is (PL / (1.0 + exp(-X * Slope)))+ ((1-PR) / (1.0 + exp(X * Slope))), run_sp(Tree, Slope, P, PF). run_grad([], _I, _, G0, G0). run_grad([gnodep(P,G, X, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :- P is (PL / (1.0 + exp(-X * Slope)))+ (PR / (1.0 + exp(X * Slope))), G0 is (GL / (1.0 + exp(-X * Slope)))+ (GR / (1.0 + exp(X * Slope))), ( I == Id -> G is G0+(PL-PR)*(1.0 / (1.0 + exp(-X * Slope)))*(1.0 / (1.0 + exp(X * Slope))) ; G = G0 ), run_grad(Tree, I, Slope, G, GF). run_grad([gnoden(P,G, X, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :- P is (PL / (1.0 + exp(-X * Slope)))+ ((1-PR) / (1.0 + exp(X * Slope))), G0 is (GL / (1.0 + exp(-X * Slope)))- (-GR / (1.0 + exp(X * Slope))), ( I == Id -> G is G0+(PL-(1-PR))*(1.0 / (1.0 + exp(-X * Slope)))*(1.0 / (1.0 + exp(X * Slope))) ; G = G0 ), run_grad(Tree, I, Slope, G, GF). %======================================================================== %= This predicate reads probability and gradient values from the file %= the gradient ID is a mere check to uncover hidden bugs %= +Filename +QueryID -QueryProbability %======================================================================== my_load(File,QueryID) :- open(File,'read',Handle), read(Handle,Atom), once(my_load_intern(Atom,Handle,QueryID)), close(Handle). my_load(File,QueryID) :- format(user_error,'Error at ~q.~2n',[my_load(File,QueryID)]), throw(error(my_load(File,QueryID))). my_load_intern(end_of_file,_,_) :- !. my_load_intern(query_probability(QueryID,Prob),Handle,QueryID) :- !, assertz(query_probability_intern(QueryID,Prob)), read(Handle,X), my_load_intern(X,Handle,QueryID). my_load_intern(query_gradient(QueryID,XFactID,Type,Value),Handle,QueryID) :- !, atomic_concat(x,StringFactID,XFactID), atom_number(StringFactID,FactID), assertz(query_gradient_intern(QueryID,FactID,Type,Value)), read(Handle,X), my_load_intern(X,Handle,QueryID). my_load_intern(X,Handle,QueryID) :- format(user_error,'Unknown atom ~q in results file.~n',[X]), read(Handle,X2), my_load_intern(X2,Handle,QueryID). %======================================================================== %= %= %= %======================================================================== query_probability(QueryID,Prob) :- ( query_probability_intern(QueryID,Prob) -> true; ( query_is_similar(QueryID,OtherQueryID), query_probability_intern(OtherQueryID,Prob) ) ). query_gradient(QueryID,Fact,Type,Value) :- ( query_gradient_intern(QueryID,Fact,Type,Value) -> true; ( query_is_similar(QueryID,OtherQueryID), query_gradient_intern(OtherQueryID,Fact,Type,Value) ) ). %======================================================================== %= %= %= %======================================================================== % FIXME ground_truth_difference :- findall(Diff,(tunable_fact(FactID,GroundTruth), \+continuous_fact(FactID), \+ var(GroundTruth), get_fact_probability(FactID,Prob), Diff is abs(GroundTruth-Prob)),AllDiffs), ( AllDiffs=[] -> ( MinDiff=0.0, MaxDiff=0.0, DiffMean=0.0 ) ; ( length(AllDiffs,Len), sum_list(AllDiffs,AllDiffsSum), min_list(AllDiffs,MinDiff), max_list(AllDiffs,MaxDiff), DiffMean is AllDiffsSum/Len ) ), logger_set_variable(ground_truth_diff,DiffMean), logger_set_variable(ground_truth_mindiff,MinDiff), logger_set_variable(ground_truth_maxdiff,MaxDiff). %======================================================================== %= Calculates the mse of training and test data %= %= -Float %======================================================================== mse_trainingset_only_for_linesearch(MSE) :- 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 :- 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) ) ) ). forget_old_probabilities :- 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,_) ) ) ). add_gradient(Learning_Rate) :- 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), 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). 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, %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % start set gradient to zero %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 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) ) ) ), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 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), 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 tunable_fact(FactID,_), ( continuous_fact(FactID) -> ( atomic_concat(['grad_mu_',FactID],Key), atomic_concat(['grad_sigma_',FactID],Key2), % if the following query fails, % it means, the fact is not used in the proof % of QueryID, and the gradient is 0.0 and will % not contribute to NewValue either way % DON'T FORGET THIS IF YOU CHANGE SOMETHING HERE! query_gradient(QueryID,FactID,mu,GradValueMu), query_gradient(QueryID,FactID,sigma,GradValueSigma), bb_get(Key,OldValueMu), bb_get(Key2,OldValueSigma), NewValueMu is OldValueMu + Y*GradValueMu, NewValueSigma is OldValueSigma + Y*GradValueSigma, bb_put(Key,NewValueMu), bb_put(Key2,NewValueSigma) ); ( atomic_concat(['grad_',FactID],Key), % if the following query fails, % it means, the fact is not used in the proof % of QueryID, and the gradient is 0.0 and will % not contribute to NewValue either way % DON'T FORGET THIS IF YOU CHANGE SOMETHING HERE! query_gradient(QueryID,FactID,p,GradValue), bb_get(Key,OldValue), NewValue is OldValue + Y*GradValue, bb_put(Key,NewValue) ) ), fail; % go to next fact true ), once(update_query_cleanup(QueryID)) )), %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 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(alpha,problog_flag_validate_number,'weight of negative examples (auto=n_p/n_n)',auto,learning_general,flags:auto_handler), problog_define_flag(sigmoid_slope,problog_flag_validate_posnumber,'slope of sigmoid function',1.0,learning_general), problog_define_flag(learning_rate,problog_flag_validate_posnumber,'Default learning rate (If line_search=false)',examples,learning_line_search,flags:examples_handler), problog_define_flag(line_search, problog_flag_validate_boolean,'estimate learning rate by line search',false,learning_line_search), problog_define_flag(line_search_never_stop, problog_flag_validate_boolean,'make tiny step if line search returns 0',true,learning_line_search), problog_define_flag(line_search_tau, problog_flag_validate_indomain_0_1_open,'tau value for line search',0.618033988749,learning_line_search), problog_define_flag(line_search_tolerance,problog_flag_validate_posnumber,'tolerance value for line search',0.05,learning_line_search), problog_define_flag(line_search_interval, problog_flag_validate_dummy,'interval for line search',(0,100),learning_line_search,flags:linesearch_interval_handler). init_logger :- logger_define_variable(iteration, int), logger_define_variable(duration,time), logger_define_variable(mse_trainingset,float), logger_define_variable(mse_min_trainingset,float), logger_define_variable(mse_max_trainingset,float), logger_define_variable(mse_testset,float), logger_define_variable(mse_min_testset,float), logger_define_variable(mse_max_testset,float), logger_define_variable(gradient_mean,float), logger_define_variable(gradient_min,float), logger_define_variable(gradient_max,float), logger_define_variable(ground_truth_diff,float), logger_define_variable(ground_truth_mindiff,float), logger_define_variable(ground_truth_maxdiff,float), logger_define_variable(learning_rate,float), logger_define_variable(alpha,float), logger_define_variable(llh_training_queries,float), logger_define_variable(llh_test_queries,float). :- initialization(init_flags). :- initialization(init_logger).