499 lines
18 KiB
Prolog
499 lines
18 KiB
Prolog
%%% -*- Mode: Prolog; -*-
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%
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% $Date: 2009-07-31 14:57:09 +0200 (Fri, 31 Jul 2009) $
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% $Revision: 1826 $
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%
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% This file is part of ProbLog
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% http://dtai.cs.kuleuven.be/problog
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%
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% ProbLog was developed at Katholieke Universiteit Leuven
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%
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% Copyright 2009
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% Angelika Kimmig, Vitor Santos Costa, Bernd Gutmann
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%
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% Main authors of this file:
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% Bernd Gutmann
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%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%
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% Artistic License 2.0
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%
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% Copyright (c) 2000-2006, The Perl Foundation.
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%
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% Everyone is permitted to copy and distribute verbatim copies of this
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% license document, but changing it is not allowed. Preamble
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%
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% This license establishes the terms under which a given free software
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% Package may be copied, modified, distributed, and/or
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% redistributed. The intent is that the Copyright Holder maintains some
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% artistic control over the development of that Package while still
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% keeping the Package available as open source and free software.
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%
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% You are always permitted to make arrangements wholly outside of this
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% license directly with the Copyright Holder of a given Package. If the
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% terms of this license do not permit the full use that you propose to
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% make of the Package, you should contact the Copyright Holder and seek
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% a different licensing arrangement. Definitions
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% "Copyright Holder" means the individual(s) or organization(s) named in
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% the copyright notice for the entire Package.
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% "Contributor" means any party that has contributed code or other
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% "You" and "your" means any person who would like to copy, distribute,
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% "Package" means the collection of files distributed by the Copyright
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%
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% Permission for Use and Modification Without Distribution
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% Modified Versions for any purpose without restriction, provided that
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% you do not Distribute the Modified Version.
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% all of the original copyright notices and associated disclaimers. At
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% your discretion, such verbatim copies may or may not include a
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% Compiled form of the Package.
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% modifications made available from the Copyright Holder. The resulting
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% Package will still be considered the Standard Version, and as such
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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:- module(flags_learning, [set_learning_flag/2,
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learning_flag/2,
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learning_flags/0]).
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:- style_check(all).
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:- yap_flag(unknown,error).
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:- use_module(logger).
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:- use_module('../problog/flags').
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:- use_module('../problog/print').
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:- ensure_loaded(library(system)).
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:- dynamic init_method/5.
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:- dynamic rebuild_bdds/1.
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:- dynamic reuse_initialized_bdds/1.
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:- dynamic learning_rate/1.
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:- dynamic probability_initializer/3.
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:- dynamic check_duplicate_bdds/1.
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:- dynamic output_directory/1.
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:- dynamic query_directory/1.
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:- dynamic log_frequency/1.
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:- dynamic alpha/1.
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:- dynamic sigmoid_slope/1.
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:- dynamic line_search/1.
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:- dynamic line_search_tolerance/1.
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:- dynamic line_search_tau/1.
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:- dynamic line_search_never_stop/1.
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:- dynamic line_search_interval/2.
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:- dynamic verbosity_level/1.
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% global parameters that can be set using set_learning_flag/2
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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learning_flag(Flag,Option) :-
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get_learning_flag(Flag,Option).
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get_learning_flag(init_method,(Query,Probability,BDDFile,ProbFile,Call)) :-
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init_method(Query,Probability,BDDFile,ProbFile,Call).
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get_learning_flag(rebuild_bdds,Iteration) :-
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rebuild_bdds(Iteration).
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get_learning_flag(reuse_initialized_bdds,Flag) :-
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reuse_initialized_bdds(Flag).
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get_learning_flag(learning_rate,R) :-
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learning_rate(R).
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get_learning_flag(probability_initializer,(FactID,Probability,Query)) :-
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probability_initializer(FactID,Probability,Query).
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get_learning_flag(check_duplicate_bdds,Flag) :-
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check_duplicate_bdds(Flag).
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get_learning_flag(output_directory,Directory) :-
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output_directory(Directory).
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get_learning_flag(query_directory,Directory) :-
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query_directory(Directory).
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get_learning_flag(log_frequency,Frequency) :-
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log_frequency(Frequency).
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get_learning_flag(alpha,Alpha) :-
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alpha(Alpha).
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get_learning_flag(sigmoid_slope,Slope) :-
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sigmoid_slope(Slope).
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get_learning_flag(line_search,Flag) :-
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line_search(Flag).
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get_learning_flag(line_search_tolerance,Tolerance) :-
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line_search_tolerance(Tolerance).
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get_learning_flag(line_search_interval,(L,R)) :-
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line_search_interval(L,R).
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get_learning_flag(line_search_tau,Tau) :-
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line_search_tau(Tau).
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get_learning_flag(line_search_never_stop,Flag) :-
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line_search_never_stop(Flag).
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get_learning_flag(verbosity_level,Number) :-
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verbosity_level(Number).
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set_learning_flag(init_method,(Query,Probability,BDDFile,ProbFile,Call)) :-
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retractall(init_method(_,_,_,_,_)),
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assert(init_method(Query,Probability,BDDFile,ProbFile,Call)).
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set_learning_flag(rebuild_bdds,Frequency) :-
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integer(Frequency),
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Frequency>=0,
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retractall(rebuild_bdds(_)),
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assert(rebuild_bdds(Frequency)).
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set_learning_flag(reuse_initialized_bdds,Flag) :-
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(Flag==true;Flag==false),
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!,
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retractall(reuse_initialized_bdds(_)),
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assert(reuse_initialized_bdds(Flag)).
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set_learning_flag(learning_rate,V) :-
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(V=examples -> true;(number(V),V>=0)),
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!,
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retractall(learning_rate(_)),
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assert(learning_rate(V)).
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set_learning_flag(probability_initializer,(FactID,Probability,Query)) :-
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var(FactID),
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var(Probability),
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callable(Query),
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retractall(probability_initializer(_,_,_)),
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assert(probability_initializer(FactID,Probability,Query)).
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set_learning_flag(check_duplicate_bdds,Flag) :-
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(Flag==true;Flag==false),
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!,
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retractall(check_duplicate_bdds(_)),
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assert(check_duplicate_bdds(Flag)).
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set_learning_flag(output_directory,Directory) :-
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(
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file_exists(Directory)
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->
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file_property(Directory,type(directory));
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make_directory(Directory)
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),
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absolute_file_name(Directory,Path),
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atomic_concat([Path,'/'],PathSlash),
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atomic_concat([Path,'/log.dat'],Log_File),
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retractall(output_directory(_)),
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assert(output_directory(PathSlash)),
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logger_set_filename(Log_File),
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set_problog_flag(dir,Directory).
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set_learning_flag(query_directory,Directory) :-
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(
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file_exists(Directory)
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->
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file_property(Directory,type(directory));
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make_directory(Directory)
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),
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absolute_file_name(Directory,Path),
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atomic_concat([Path,'/'],PathSlash),
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retractall(query_directory(_)),
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assert(query_directory(PathSlash)).
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set_learning_flag(log_frequency,Frequency) :-
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integer(Frequency),
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Frequency>=0,
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retractall(log_frequency(_)),
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assert(log_frequency(Frequency)).
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set_learning_flag(alpha,Alpha) :-
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(number(Alpha);Alpha==auto),
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!,
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retractall(alpha(_)),
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assert(alpha(Alpha)).
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set_learning_flag(sigmoid_slope,Slope) :-
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number(Slope),
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Slope>0,
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retractall(sigmoid_slope(_)),
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assert(sigmoid_slope(Slope)).
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set_learning_flag(line_search,Flag) :-
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(Flag==true;Flag==false),
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!,
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retractall(line_search(_)),
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assert(line_search(Flag)).
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set_learning_flag(line_search_tolerance,Number) :-
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number(Number),
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Number>0,
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retractall(line_search_tolerance(_)),
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assert(line_search_tolerance(Number)).
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set_learning_flag(line_search_interval,(L,R)) :-
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number(L),
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number(R),
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L<R,
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retractall(line_search_interval(_,_)),
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assert(line_search_interval(L,R)).
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set_learning_flag(line_search_tau,Number) :-
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number(Number),
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Number>0,
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retractall(line_search_tau(_)),
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assert(line_search_tau(Number)).
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set_learning_flag(line_search_never_stop,Flag) :-
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(Flag==true;Flag==false),
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!,
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retractall(line_search_nerver_stop(_)),
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assert(line_search_never_stop(Flag)).
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set_learning_flag(verbosity_level,Level) :-
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integer(Level),
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retractall(verbosity_level(_)),
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assert(verbosity_level(Level)),
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(
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Level<4
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->
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set_problog_flag(verbose,false);
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set_problog_flag(verbose,true)
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).
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%%%%%%%%%%%%%%%%%%%%%%%%
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% show values
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%%%%%%%%%%%%%%%%%%%%%%%%
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skolemize(T1,T2):-
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copy_term(T1,T2),
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numbervars(T2,0,_).
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learning_flags :-
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format('~n',[]),
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print_sep_line,
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format('learning flags: use set_learning_flag(Flag,Option) to change, learning_flag(Flag,Option) to view~n',[]),
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print_sep_line,
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print_param(description,value,flag,option),
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print_sep_line,
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learning_flag(output_directory,Output_Directory),
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print_long_param('Where to store results',Output_Directory,'output_directory','path'),
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learning_flag(query_directory,Query_Directory),
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print_long_param('Where to store BDD files',Query_Directory,'query_directory','path'),
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learning_flag(verbosity_level,Verbosity_Level),
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print_param('How much output shall be given (0=nothing,5=all)',Verbosity_Level,'verbosity_level','0,1,..,5'),
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print_sep_line,
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learning_flag(reuse_initialized_bdds,Reuse_Initialized_Bdds),
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print_param('Reuse BDDs from previous runs',Reuse_Initialized_Bdds,'reuse_initialized_bdds','true/false'),
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learning_flag(rebuild_bdds,Rebuild_BDDs),
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print_param('Rebuild BDDs every nth iteration (0=never)',Rebuild_BDDs,'rebuild_bdds','Integer>=0'),
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learning_flag(check_duplicate_bdds,Check_Duplicate_BDDs),
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print_param('Store intermediate results in hash table',Check_Duplicate_BDDs,'check_duplicate_bdds','true/false'),
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learning_flag(init_method,Init_Method),
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skolemize(Init_Method,Init_Method_SK),
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print_long_param('ProbLog predicate to search proofs',Init_Method_SK,'init_method','(+Query,-P,+BDDFile,+ProbFile,+Call)'),
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learning_flag(probability_initializer,Prob_Initializer),
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skolemize(Prob_Initializer,Prob_Initializer_SK),
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print_long_param('Predicate to initialize probabilities',Prob_Initializer_SK,'probability_initializer','(+FactID,-P,+Call)'),
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print_sep_line,
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learning_flag(log_frequency,Log_Frequency),
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print_param('log results every nth iteration',Log_Frequency,'log_frequency','integer>0'),
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learning_flag(alpha,Alpha),
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print_param('weight of negative examples (auto=n_p/n_n)',Alpha,'alpha','number or "auto"'),
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learning_flag(sigmoid_slope,Slope),
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print_param('slope of sigmoid function',Slope,'slope','number>0'),
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print_sep_line,
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learning_flag(learning_rate,Learning_Rate),
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print_param('Default Learning rate (If line_search=false)',Learning_Rate,'learning_rate','0<Number or "examples"'),
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learning_flag(line_search,Line_Search),
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print_param('Use line search to estimate learning rate',Line_Search,'line_search','true/false'),
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learning_flag(line_search_tau,Line_Search_Tau),
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print_param('Tau value for line search',Line_Search_Tau,'line_search_tau','0<Number<1'),
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learning_flag(line_search_tolerance,Line_Search_Tolerance),
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print_param('Tolerance value for line search',Line_Search_Tolerance,'line_search_tolerance','0<Number'),
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learning_flag(line_search_interval,Line_Search_Interval),
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print_param('Interval for line search',Line_Search_Interval,'line_search_interval','(a,b) an interval with 0<=a<b'),
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learning_flag(line_search_never_stop,Line_Search_Never_Stop),
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print_param('Make tiny step if line search returns 0',Line_Search_Never_Stop,'line_search_never_stop','true/false'),
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print_sep_line,
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format('~n',[]),
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flush_output.
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