%%% -*- Mode: Prolog; -*- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % $Date: 2009-07-31 14:57:09 +0200 (Fri, 31 Jul 2009) $ % $Revision: 1826 $ % % 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(flags_learning, [set_learning_flag/2, learning_flag/2, learning_flags/0]). :- style_check(all). :- yap_flag(unknown,error). :- use_module(logger). :- use_module('../problog/flags'). :- use_module('../problog/print'). :- ensure_loaded(library(system)). :- dynamic init_method/5. :- dynamic rebuild_bdds/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. :- dynamic verbosity_level/1. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % global parameters that can be set using set_learning_flag/2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% learning_flag(Flag,Option) :- get_learning_flag(Flag,Option). get_learning_flag(init_method,(Query,Probability,BDDFile,ProbFile,Call)) :- init_method(Query,Probability,BDDFile,ProbFile,Call). get_learning_flag(rebuild_bdds,Iteration) :- rebuild_bdds(Iteration). get_learning_flag(reuse_initialized_bdds,Flag) :- reuse_initialized_bdds(Flag). get_learning_flag(learning_rate,R) :- learning_rate(R). get_learning_flag(probability_initializer,(FactID,Probability,Query)) :- probability_initializer(FactID,Probability,Query). get_learning_flag(check_duplicate_bdds,Flag) :- check_duplicate_bdds(Flag). get_learning_flag(output_directory,Directory) :- output_directory(Directory). get_learning_flag(query_directory,Directory) :- query_directory(Directory). get_learning_flag(log_frequency,Frequency) :- log_frequency(Frequency). get_learning_flag(alpha,Alpha) :- alpha(Alpha). get_learning_flag(sigmoid_slope,Slope) :- sigmoid_slope(Slope). get_learning_flag(line_search,Flag) :- line_search(Flag). get_learning_flag(line_search_tolerance,Tolerance) :- line_search_tolerance(Tolerance). get_learning_flag(line_search_interval,(L,R)) :- line_search_interval(L,R). get_learning_flag(line_search_tau,Tau) :- line_search_tau(Tau). get_learning_flag(line_search_never_stop,Flag) :- line_search_never_stop(Flag). get_learning_flag(verbosity_level,Number) :- verbosity_level(Number). 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,Frequency) :- integer(Frequency), Frequency>=0, retractall(rebuild_bdds(_)), assert(rebuild_bdds(Frequency)). 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) ), absolute_file_name(Directory,Path), atomic_concat([Path,'/'],PathSlash), atomic_concat([Path,'/log.dat'],Log_File), retractall(output_directory(_)), assert(output_directory(PathSlash)), logger_set_filename(Log_File), set_problog_flag(dir,Directory). set_learning_flag(query_directory,Directory) :- ( file_exists(Directory) -> file_property(Directory,type(directory)); make_directory(Directory) ), absolute_file_name(Directory,Path), atomic_concat([Path,'/'],PathSlash), retractall(query_directory(_)), assert(query_directory(PathSlash)). set_learning_flag(log_frequency,Frequency) :- integer(Frequency), Frequency>=0, retractall(log_frequency(_)), assert(log_frequency(Frequency)). set_learning_flag(alpha,Alpha) :- (number(Alpha);Alpha==auto), !, 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)). set_learning_flag(verbosity_level,Level) :- integer(Level), retractall(verbosity_level(_)), assert(verbosity_level(Level)), ( Level<4 -> set_problog_flag(verbose,false); set_problog_flag(verbose,true) ). %%%%%%%%%%%%%%%%%%%%%%%% % show values %%%%%%%%%%%%%%%%%%%%%%%% skolemize(T1,T2):- copy_term(T1,T2), numbervars(T2,0,_). learning_flags :- format('~n',[]), print_sep_line, format('learning flags: use set_learning_flag(Flag,Option) to change, learning_flag(Flag,Option) to view~n',[]), print_sep_line, print_param(description,value,flag,option), print_sep_line, learning_flag(output_directory,Output_Directory), print_long_param('Where to store results',Output_Directory,'output_directory','path'), learning_flag(query_directory,Query_Directory), print_long_param('Where to store BDD files',Query_Directory,'query_directory','path'), learning_flag(verbosity_level,Verbosity_Level), print_param('How much output shall be given (0=nothing,5=all)',Verbosity_Level,'verbosity_level','0,1,..,5'), print_sep_line, learning_flag(reuse_initialized_bdds,Reuse_Initialized_Bdds), print_param('Reuse BDDs from previous runs',Reuse_Initialized_Bdds,'reuse_initialized_bdds','true/false'), learning_flag(rebuild_bdds,Rebuild_BDDs), print_param('Rebuild BDDs every nth iteration (0=never)',Rebuild_BDDs,'rebuild_bdds','Integer>=0'), learning_flag(check_duplicate_bdds,Check_Duplicate_BDDs), print_param('Store intermediate results in hash table',Check_Duplicate_BDDs,'check_duplicate_bdds','true/false'), learning_flag(init_method,Init_Method), skolemize(Init_Method,Init_Method_SK), print_long_param('ProbLog predicate to search proofs',Init_Method_SK,'init_method','(+Query,-P,+BDDFile,+ProbFile,+Call)'), learning_flag(probability_initializer,Prob_Initializer), skolemize(Prob_Initializer,Prob_Initializer_SK), print_long_param('Predicate to initialize probabilities',Prob_Initializer_SK,'probability_initializer','(+FactID,-P,+Call)'), print_sep_line, learning_flag(log_frequency,Log_Frequency), print_param('log results every nth iteration',Log_Frequency,'log_frequency','integer>0'), learning_flag(alpha,Alpha), print_param('weight of negative examples (auto=n_p/n_n)',Alpha,'alpha','number or "auto"'), learning_flag(sigmoid_slope,Slope), print_param('slope of sigmoid function',Slope,'slope','number>0'), print_sep_line, learning_flag(learning_rate,Learning_Rate), print_param('Default Learning rate (If line_search=false)',Learning_Rate,'learning_rate','0