1706 lines
48 KiB
Prolog
1706 lines
48 KiB
Prolog
%%% -*- Mode: Prolog; -*-
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%
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% $Date: 2009-06-17 22:22:00 +0200 (Mi, 17 Jun 2009) $
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% $Revision: 1550 $
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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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% Copyright 2009 Katholieke Universiteit Leuven
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%
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% Authors: Luc De Raedt, Bernd Gutmann, Angelika Kimmig,
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% Vitor Santos Costa
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%
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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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:- module(learning,[do_learning/1,
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do_learning/2,
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set_learning_flag/2,
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save_model/1,
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problog_help/0,
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set_problog_flag/2,
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problog_flag/2,
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problog_flags/0
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]).
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% switch on all the checks to reduce bug searching time
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:- style_check(all).
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:- yap_flag(unknown,error).
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% load modules from the YAP library
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:- use_module(library(lists)).
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:- use_module(library(random),[random/1]).
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:- use_module(library(system),[file_exists/1,
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file_property/2,
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delete_file/1,
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make_directory/1,
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working_directory/2,
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shell/1,
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shell/2]).
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% load our own modules
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:- use_module(library('problog_learning/logger')).
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:- use_module(library(problog)).
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% used to indicate the state of the system
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:- dynamic values_correct/0.
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:- dynamic learning_initialized/0.
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:- dynamic current_iteration/1.
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:- dynamic example_count/1.
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:- dynamic query_probability_intern/2.
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:- dynamic query_gradient_intern/3.
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:- dynamic last_mse/1.
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% used to identify queries which have identical proofs
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:- dynamic query_is_similar/2.
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:- dynamic query_md5/2.
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% used by set_learning_flag
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:- dynamic init_method/5.
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:- dynamic rebuild_bdds/1.
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:- dynamic rebuild_bdds_it/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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%==========================================================================
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%= You can set some flags and parameters
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%=
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%= init_method/5 specifies which ProbLog inference mechanism is used
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%= to answer queries
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%=
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%=
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%= if rebuild_bdds(true) is set, the bdds are rebuild after
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%= each N iterations for rebuild_bdds_it(N)
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%=
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%= if reuse_initialized_bdds(true) is set, the bdds which are on the
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%= harddrive from the previous run of LeProbLog are reused.
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%= do not use this, when you changed the init method in the meantime
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%=
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%==========================================================================
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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,Flag) :-
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(Flag=true;Flag=false),
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!,
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retractall(rebuild_bdds(_)),
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assert(rebuild_bdds(Flag)).
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set_learning_flag(rebuild_bdds_it,Flag) :-
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integer(Flag),
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retractall(rebuild_bdds_it(_)),
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assert(rebuild_bdds_it(Flag)).
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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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working_directory(PWD,PWD),
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atomic_concat([PWD,'/',Directory,'/'],Path),
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atomic_concat([Directory,'/log.dat'],Logfile),
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retractall(output_directory(_)),
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assert(output_directory(Path)),
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logger_set_filename(Logfile),
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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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working_directory(PWD,PWD),
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atomic_concat([PWD,'/',Directory,'/'],Path),
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retractall(query_directory(_)),
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assert(query_directory(Path)).
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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),
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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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|
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%========================================================================
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%= store the facts with the learned probabilities to a file
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%= if F is a variable, a filename based on the current iteration is used
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%=
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%========================================================================
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save_model(F) :-
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(
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var(F)
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->
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(
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current_iteration(Iteration),
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output_directory(Directory),
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atomic_concat([Directory,'factprobs_',Iteration,'.pl'],F)
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);true
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),
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export_facts(F).
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%========================================================================
|
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%= store the probabilities for all training and test examples
|
|
%= if F is a variable, a filename based on the current iteration is used
|
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%=
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%========================================================================
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save_predictions(F) :-
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update_values,
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current_iteration(Iteration),
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(
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var(F)
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->
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(
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current_iteration(Iteration),
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output_directory(Directory),
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atomic_concat([Directory,'predictions_',Iteration,'.pl'],F)
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);true
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),
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|
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open(F,'append',Handle),
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format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",[]),
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format(Handle,"% Iteration, train/test, QueryID, Query, GroundTruth, Prediction %\n",[]),
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format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",[]),
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!,
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( % go over all training examples
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current_predicate(user:example/3),
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user:example(Query_ID,Query,TrueQueryProb),
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query_probability(Query_ID,LearnedQueryProb),
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format(Handle,'ex(~q,train,~q,~q,~10f,~10f).\n',
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[Iteration,Query_ID,Query,TrueQueryProb,LearnedQueryProb]),
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fail; % go to next training example
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true
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),
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( % go over all test examples
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current_predicate(user:test_example/3),
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user:test_example(Query_ID,Query,TrueQueryProb),
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query_probability(Query_ID,LearnedQueryProb),
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format(Handle,'ex(~q,test,~q,~q,~10f,~10f).\n',
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[Iteration,Query_ID,Query,TrueQueryProb,LearnedQueryProb]),
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fail; % go to next test example
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true
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),
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format(Handle,'~3n',[]),
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close(Handle).
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|
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%========================================================================
|
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%= find out whether some example IDs are used more than once
|
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%= if so, complain and stop
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%=
|
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%========================================================================
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|
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check_examples :-
|
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(
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(
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(current_predicate(user:example/3),user:example(ID,_,_), \+ atomic(ID)) ;
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(current_predicate(user:test_example/3),user:test_example(ID,_,_), \+ atomic(ID))
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)
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->
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(
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format(user_error,'The example id of example ~q is not atomic (e.g foo42, 23, bar, ...).~n',[ID]),
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|
throw(error(examples))
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); true
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),
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|
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(
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(
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(current_predicate(user:example/3),user:example(ID,_,P), (\+ number(P); P>1 ; P<0));
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(current_predicate(user:test_example/3),user:test_example(ID,_,P), (\+ number(P) ; P>1 ; P<0))
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)
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->
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(
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|
format(user_error,'The example ~q does not have a valid probaility value (~q).~n',[ID,P]),
|
|
throw(error(examples))
|
|
); true
|
|
),
|
|
|
|
|
|
(
|
|
(
|
|
(
|
|
current_predicate(user:example/3),
|
|
user:example(ID,QueryA,_),
|
|
user:example(ID,QueryB,_),
|
|
QueryA \= QueryB
|
|
) ;
|
|
|
|
(
|
|
current_predicate(user:test_example/3),
|
|
user:test_example(ID,QueryA,_),
|
|
user:test_example(ID,QueryB,_),
|
|
QueryA \= QueryB
|
|
);
|
|
|
|
(
|
|
current_predicate(user:example/3),
|
|
current_predicate(user:test_example/3),
|
|
user:example(ID,QueryA,_),
|
|
user:test_example(ID,QueryB,_),
|
|
QueryA \= QueryB
|
|
)
|
|
)
|
|
->
|
|
(
|
|
format(user_error,'The example id ~q is used several times.~n',[ID]),
|
|
throw(error(examples))
|
|
); true
|
|
).
|
|
|
|
|
|
|
|
|
|
|
|
|
|
%========================================================================
|
|
%= initialize everything and perform Iterations times gradient descent
|
|
%= can be called several times
|
|
%= if it is called with an epsilon parameter, it stops when the change
|
|
%= in the MSE is smaller than epsilon
|
|
%========================================================================
|
|
|
|
do_learning(Iterations) :-
|
|
integer(Iterations),
|
|
|
|
(
|
|
current_predicate(user:example/3)
|
|
->
|
|
true;
|
|
format(user_error,'~n~nWarning: No training examples specified !!!~n~n',[])
|
|
),
|
|
|
|
do_learning_intern(Iterations,-1).
|
|
|
|
do_learning(Iterations,Epsilon) :-
|
|
integer(Iterations),
|
|
float(Epsilon),
|
|
|
|
Iterations>0,
|
|
Epsilon>0.0,
|
|
|
|
(
|
|
current_predicate(user:example/3)
|
|
->
|
|
true;
|
|
format(user_error,'~n~nWarning: No training examples specified !!!~n~n',[])
|
|
),
|
|
|
|
do_learning_intern(Iterations,Epsilon).
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
do_learning_intern(Iterations,Epsilon) :-
|
|
(
|
|
Iterations=0
|
|
->
|
|
true;
|
|
(
|
|
Iterations>0,
|
|
|
|
% nothing will happen, if we're already initialized
|
|
init_learning,
|
|
current_iteration(OldIteration),
|
|
!,
|
|
retractall(current_iteration(_)),
|
|
!,
|
|
CurrentIteration is OldIteration+1,
|
|
assert(current_iteration(CurrentIteration)),
|
|
EndIteration is OldIteration+Iterations,
|
|
|
|
format('~n Iteration ~d of ~d~n',[CurrentIteration,EndIteration]),
|
|
logger_set_variable(iteration,CurrentIteration),
|
|
|
|
logger_start_timer(duration),
|
|
gradient_descent,
|
|
|
|
(
|
|
(rebuild_bdds(true),rebuild_bdds_it(BDDFreq),0 =:= CurrentIteration mod BDDFreq)
|
|
->
|
|
(
|
|
once(delete_all_queries),
|
|
once(init_queries)
|
|
); true
|
|
),
|
|
|
|
|
|
mse_trainingset,
|
|
mse_testset,
|
|
|
|
(
|
|
last_mse(Last_MSE)
|
|
->
|
|
(
|
|
retractall(last_mse(_)),
|
|
logger_get_variable(mse_trainingset,Current_MSE),
|
|
assert(last_mse(Current_MSE)),
|
|
!,
|
|
MSE_Diff is abs(Last_MSE-Current_MSE)
|
|
); (
|
|
logger_get_variable(mse_trainingset,Current_MSE),
|
|
assert(last_mse(Current_MSE)),
|
|
MSE_Diff is Epsilon+1
|
|
)
|
|
),
|
|
|
|
!,
|
|
logger_stop_timer(duration),
|
|
once(ground_truth_difference),
|
|
|
|
logger_write_data,
|
|
|
|
|
|
log_frequency(Log_Frequency),
|
|
|
|
(
|
|
( Log_Frequency=0; 0 =:= CurrentIteration mod Log_Frequency)
|
|
->
|
|
(
|
|
save_predictions(_X),
|
|
save_model(_Y)
|
|
);
|
|
true
|
|
),
|
|
RemainingIterations is Iterations-1,
|
|
|
|
(
|
|
MSE_Diff>Epsilon
|
|
->
|
|
do_learning_intern(RemainingIterations,Epsilon);
|
|
true
|
|
)
|
|
)
|
|
).
|
|
|
|
|
|
|
|
%========================================================================
|
|
%= find proofs and build bdds for all training and test examples
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
|
|
init_learning :-
|
|
(
|
|
learning_initialized
|
|
->
|
|
true;
|
|
(
|
|
|
|
check_examples,
|
|
|
|
|
|
format('Delete previous logs (if existing) from output directory~2n',[]),
|
|
empty_output_directory,
|
|
|
|
format('Initializing everything~n',[]),
|
|
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% Delete the BDDs from the previous run if they should
|
|
% not be reused
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
|
|
|
|
(
|
|
(reuse_initialized_bdds(false);rebuild_bdds(true))
|
|
->
|
|
delete_all_queries;
|
|
true
|
|
),
|
|
|
|
|
|
logger_write_header,
|
|
logger_start_timer(duration),
|
|
logger_set_variable(iteration,0),
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% start count examples
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
bb_put(training_examples,0),
|
|
( % go over all training examples
|
|
current_predicate(user:example/3),
|
|
user:example(_,_,_),
|
|
bb_get(training_examples, OldCounter),
|
|
NewCounter is OldCounter+1,
|
|
bb_put(training_examples,NewCounter),
|
|
fail;
|
|
true
|
|
),
|
|
|
|
bb_put(test_examples,0),
|
|
( % go over all test examples
|
|
current_predicate(user:test_example/3),
|
|
user:test_example(_,_,_),
|
|
bb_get(test_examples, OldCounter),
|
|
NewCounter is OldCounter+1,
|
|
bb_put(test_examples,NewCounter),
|
|
fail;
|
|
true
|
|
),
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% stop count examples
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
!,
|
|
|
|
bb_delete(training_examples,TrainingExampleCount),
|
|
bb_delete(test_examples,TestExampleCount),
|
|
|
|
assert(example_count(TrainingExampleCount)),
|
|
|
|
(
|
|
learning_rate(examples)
|
|
->
|
|
set_learning_flag(learning_rate,TrainingExampleCount);
|
|
true
|
|
),
|
|
learning_rate(Learning_Rate),
|
|
format('~q training examples found.~n~q test examples found.~nlearning rate=~f~n~n',
|
|
[TrainingExampleCount,TestExampleCount,Learning_Rate]),
|
|
format('Generate BDDs for all queries in the training and test set~n',[]),
|
|
initialize_fact_probabilities,
|
|
init_queries,
|
|
|
|
format('All Queries have been generated~n',[]),
|
|
mse_trainingset,
|
|
mse_testset,
|
|
|
|
!,
|
|
logger_stop_timer(duration),
|
|
|
|
ground_truth_difference,
|
|
|
|
logger_write_data,
|
|
assert(current_iteration(0)),
|
|
assert(learning_initialized),
|
|
save_model(_),save_predictions(_)
|
|
|
|
)
|
|
).
|
|
|
|
%========================================================================
|
|
%=
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
|
|
|
|
|
|
delete_all_queries :-
|
|
query_directory(Directory),
|
|
atomic_concat(['rm -f ',Directory,'query_*'],Command),
|
|
(shell(Command) -> true; true),
|
|
retractall(query_is_similar(_,_)),
|
|
retractall(query_md5(_,_)).
|
|
|
|
empty_output_directory :-
|
|
output_directory(Directory),
|
|
atomic_concat(['rm -f ',Directory,'log.dat ',
|
|
Directory,'factprobs_*.pl ',
|
|
Directory,'predictions_*.pl'],Command),
|
|
(shell(Command) -> true; true).
|
|
|
|
%========================================================================
|
|
%= This predicate goes over all training and test examples,
|
|
%= calls the inference method of ProbLog and stores the resulting
|
|
%= BDDs
|
|
%========================================================================
|
|
|
|
|
|
init_queries :-
|
|
|
|
( % go over all training examples
|
|
current_predicate(user:example/3),
|
|
user:example(ID,Query,Prob),
|
|
format('~n~n training example ~q: ~q~n',[ID,Query]),
|
|
flush_output(user),
|
|
init_one_query(ID,Query),
|
|
fail; %go to next training example
|
|
|
|
true
|
|
),
|
|
|
|
( % go over all test examples
|
|
current_predicate(user:test_example/3),
|
|
user:test_example(ID,Query,Prob),
|
|
format('~n~n test example ~q: ~q~n',[ID,Query]),
|
|
flush_output(user),
|
|
init_one_query(ID,Query),
|
|
fail; % go to next test example
|
|
|
|
true
|
|
).
|
|
|
|
init_one_query(QueryID,Query) :-
|
|
output_directory(Output_Directory),
|
|
query_directory(Query_Directory),
|
|
|
|
atomic_concat([Query_Directory,'query_',QueryID],Filename),
|
|
atomic_concat([Output_Directory,'input.txt'],Filename2),
|
|
atomic_concat([Output_Directory,'tmp_md5'],Filename3),
|
|
|
|
(
|
|
file_exists(Filename)
|
|
->
|
|
format('Reuse existing BDD ~q~n~n',[Filename]);
|
|
(
|
|
init_method(Query,_Prob,Filename,Filename2,InitCall),
|
|
once(call(InitCall)),
|
|
delete_file(Filename2)
|
|
)
|
|
),
|
|
|
|
(
|
|
check_duplicate_bdds(true)
|
|
->
|
|
(
|
|
% calculate the md5sum of the bdd script file
|
|
atomic_concat(['cat ',Filename,' | md5sum | sed "s/ .*$/\')./" | sed "s/^/md5(\'/"> ',Filename3],MD5Command),
|
|
|
|
(shell(MD5Command,0) -> true; throw(error("Something wrong with calculating the MD5 value"))),
|
|
|
|
open(Filename3, read, Handle),
|
|
read(Handle,md5(Query_MD5)),
|
|
close(Handle),
|
|
|
|
delete_file(Filename3),
|
|
|
|
% Does another query exists which already has this MD5?
|
|
(
|
|
query_md5(OtherQueryID,Query_MD5)
|
|
->
|
|
% yippie! we can save a lot of work
|
|
(
|
|
assert(query_is_similar(QueryID,OtherQueryID)),
|
|
format('~q is similar to ~q~2n', [QueryID,OtherQueryID])
|
|
); assert(query_md5(QueryID,Query_MD5))
|
|
)
|
|
);
|
|
|
|
true
|
|
).
|
|
|
|
|
|
%========================================================================
|
|
%= set all unknown fact probabilities to random values
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
|
|
initialize_fact_probabilities :-
|
|
( % go over all tunable facts
|
|
tunable_fact(FactID,_),
|
|
|
|
probability_initializer(FactID,Probability,Query),
|
|
once(call(Query)),
|
|
set_fact_probability(FactID,Probability),
|
|
|
|
|
|
fail; % go to next tunable fact
|
|
|
|
true
|
|
).
|
|
|
|
random_probability(_FactID,Probability) :-
|
|
% use probs around 0.5 to not confuse k-best search
|
|
random(Random),
|
|
Probability is 0.5+(Random-0.5)/100.
|
|
|
|
|
|
|
|
|
|
|
|
%========================================================================
|
|
%= updates all values of query_probability/2 and query_gradient/3
|
|
%= should be called always before these predicates are accessed
|
|
%= if the old values are still valid, nothing happens
|
|
%========================================================================
|
|
|
|
update_values :-
|
|
update_values(all).
|
|
|
|
|
|
update_values(_) :-
|
|
values_correct,
|
|
!.
|
|
|
|
update_values(What_To_Update) :-
|
|
\+ values_correct,
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% delete old values
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
once(retractall(query_probability_intern(_,_))),
|
|
once(retractall(query_gradient_intern(_,_,_))),
|
|
|
|
|
|
output_directory(Directory),
|
|
atomic_concat(Directory,'input.txt',Input_Filename),
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% start write current probabilities to file
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
open(Input_Filename,'write',Handle),
|
|
|
|
( % go over all probabilistic fact
|
|
get_fact_probability(ID,Prob),
|
|
inv_sigmoid(Prob,Value),
|
|
(
|
|
non_ground_fact(ID)
|
|
->
|
|
format(Handle,'@x~q_*~n~10f~n',[ID,Value]);
|
|
format(Handle,'@x~q~n~10f~n',[ID,Value])
|
|
),
|
|
|
|
fail; % go to next probabilistic fact
|
|
true
|
|
),
|
|
|
|
close(Handle),
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% stop write current probabilities to file
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
!,
|
|
|
|
|
|
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% start update values for all test examples
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
( What_To_Update = all
|
|
->
|
|
( % go over all training examples
|
|
current_predicate(user:test_example/3),
|
|
user:test_example(QueryID,_Query,_QueryProb),
|
|
once(call_bdd_tool(QueryID,'+',all)),
|
|
fail; % go to next training example
|
|
true
|
|
); true
|
|
),
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% stop update values for all test examples
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
!,
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% start update values for all training examples
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
( % go over all training examples
|
|
current_predicate(user:example/3),
|
|
user:example(QueryID,_Query,_QueryProb),
|
|
once(call_bdd_tool(QueryID,'.',What_To_Update)),
|
|
fail; % go to next training example
|
|
true
|
|
),
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% stop update values for all training examples
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
!,
|
|
|
|
|
|
nl,
|
|
|
|
delete_file(Input_Filename),
|
|
assert(values_correct).
|
|
|
|
|
|
%========================================================================
|
|
%=
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
|
|
|
|
call_bdd_tool(QueryID,Symbol,What_To_Update) :-
|
|
output_directory(Output_Directory),
|
|
query_directory(Query_Directory),
|
|
(
|
|
query_is_similar(QueryID,_)
|
|
->
|
|
% we don't have to evaluate the BDD
|
|
write('#');
|
|
(
|
|
sigmoid_slope(Slope),
|
|
problog_dir(PD),
|
|
(What_To_Update=all -> Method='g' ; Method='l'),
|
|
atomic_concat([PD,
|
|
'/ProblogBDD -i "',
|
|
Output_Directory,
|
|
'input.txt',
|
|
'" -l "',
|
|
Query_Directory,
|
|
'query_',
|
|
QueryID,
|
|
'" -m ',Method,' -id ',
|
|
QueryID,
|
|
' -sl ',Slope,
|
|
' > "',
|
|
Output_Directory,
|
|
'values.pl"'],Command),
|
|
shell(Command,Error),
|
|
|
|
atomic_concat([' cat "',
|
|
Output_Directory,
|
|
'values.pl" >> ~/all_results'],Command2),
|
|
shell(Command2),
|
|
(
|
|
Error = 2
|
|
->
|
|
throw(error('SimpleCUDD has been interrupted.'));
|
|
true
|
|
),
|
|
(
|
|
Error \= 0
|
|
->
|
|
throw(bdd_error(QueryID,Error));
|
|
true
|
|
),
|
|
atomic_concat([Output_Directory,'values.pl'],Values_Filename),
|
|
(
|
|
file_exists(Values_Filename)
|
|
->
|
|
(
|
|
(
|
|
once(my_load(Values_Filename))
|
|
->
|
|
true;
|
|
(
|
|
format(user_error,'ERROR: Tried to read the file ~q but my_load/1 fails.~n~q.~2n',[Values_Filename,call_bdd_tool(QueryID,Symbol,What_To_Update)]),
|
|
throw(error(my_load_fails))
|
|
)
|
|
);
|
|
(
|
|
format(user_error,'ERROR: Tried to read the file ~q but it does not exist.~n~q.~2n',[Values_Filename,call_bdd_tool(QueryID,Symbol,What_To_Update)]),
|
|
throw(error(output_file_does_not_exist))
|
|
)
|
|
)
|
|
),
|
|
|
|
delete_file(Values_Filename),
|
|
write(Symbol)
|
|
)
|
|
),
|
|
flush_output(user).
|
|
|
|
|
|
%========================================================================
|
|
%=
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
|
|
my_load(File) :-
|
|
see(File),
|
|
read(X),
|
|
my_load_intern(X),
|
|
seen.
|
|
my_load_intern(end_of_file) :-
|
|
!.
|
|
my_load_intern(query_probability(QueryID,Prob)) :-
|
|
!,
|
|
assert(query_probability_intern(QueryID,Prob)),
|
|
read(X2),
|
|
my_load_intern(X2).
|
|
my_load_intern(query_gradient(QueryID,XFactID,Value)) :-
|
|
!,
|
|
atomic_concat(x,StringFactID,XFactID),
|
|
atom_number(StringFactID,FactID),
|
|
assert(query_gradient_intern(QueryID,FactID,Value)),
|
|
read(X2),
|
|
my_load_intern(X2).
|
|
my_load_intern(X) :-
|
|
format(user_error,'Unknown atom ~q in results file.~n',[X]),
|
|
read(X2),
|
|
my_load_intern(X2).
|
|
|
|
|
|
%========================================================================
|
|
%=
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
query_probability(QueryID,Prob) :-
|
|
(
|
|
query_probability_intern(QueryID,Prob)
|
|
->
|
|
true;
|
|
(
|
|
query_is_similar(QueryID,OtherQueryID),
|
|
query_probability_intern(OtherQueryID,Prob)
|
|
)
|
|
).
|
|
query_gradient(QueryID,Fact,Value) :-
|
|
(
|
|
query_gradient_intern(QueryID,Fact,Value)
|
|
->
|
|
true;
|
|
(
|
|
query_is_similar(QueryID,OtherQueryID),
|
|
query_gradient_intern(OtherQueryID,Fact,Value)
|
|
)
|
|
).
|
|
|
|
%========================================================================
|
|
%=
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
|
|
|
|
ground_truth_difference :-
|
|
findall(Diff,(tunable_fact(FactID,GroundTruth),
|
|
\+ var(GroundTruth),
|
|
get_fact_probability(FactID,Prob),
|
|
Diff is abs(GroundTruth-Prob)),AllDiffs),
|
|
|
|
% if no ground truth was specified for facts
|
|
% set everything to zero
|
|
(
|
|
AllDiffs=[]
|
|
->
|
|
(
|
|
MinDiff=0.0,
|
|
MaxDiff=0.0,
|
|
DiffMean=0.0
|
|
) ; (
|
|
length(AllDiffs,Len),
|
|
sum_list(AllDiffs,AllDiffsSum),
|
|
min_list(AllDiffs,MinDiff),
|
|
max_list(AllDiffs,MaxDiff),
|
|
|
|
DiffMean is AllDiffsSum/Len
|
|
)
|
|
),
|
|
|
|
logger_set_variable(ground_truth_diff,DiffMean),
|
|
logger_set_variable(ground_truth_mindiff,MinDiff),
|
|
logger_set_variable(ground_truth_maxdiff,MaxDiff).
|
|
|
|
%========================================================================
|
|
%= Calculates the mse of training and test data
|
|
%=
|
|
%= -Float
|
|
%========================================================================
|
|
|
|
mse_trainingset_only_for_linesearch(MSE) :-
|
|
(
|
|
current_predicate(user:example/3)
|
|
->
|
|
(
|
|
update_values(probabilities),
|
|
findall(SquaredError,
|
|
(user:example(QueryID,_Query,QueryProb),
|
|
query_probability(QueryID,CurrentProb),
|
|
SquaredError is (CurrentProb-QueryProb)**2),
|
|
AllSquaredErrors),
|
|
|
|
length(AllSquaredErrors,Length),
|
|
sum_list(AllSquaredErrors,SumAllSquaredErrors),
|
|
MSE is SumAllSquaredErrors/Length
|
|
); true
|
|
),
|
|
retractall(values_correct).
|
|
|
|
% calculate the mse of the training data
|
|
mse_trainingset :-
|
|
(
|
|
current_predicate(user:example/3)
|
|
->
|
|
(
|
|
update_values,
|
|
findall(SquaredError,
|
|
(user:example(QueryID,_Query,QueryProb),
|
|
query_probability(QueryID,CurrentProb),
|
|
SquaredError is (CurrentProb-QueryProb)**2),
|
|
AllSquaredErrors),
|
|
|
|
length(AllSquaredErrors,Length),
|
|
sum_list(AllSquaredErrors,SumAllSquaredErrors),
|
|
min_list(AllSquaredErrors,MinError),
|
|
max_list(AllSquaredErrors,MaxError),
|
|
MSE is SumAllSquaredErrors/Length,
|
|
|
|
logger_set_variable(mse_trainingset,MSE),
|
|
logger_set_variable(mse_min_trainingset,MinError),
|
|
logger_set_variable(mse_max_trainingset,MaxError)
|
|
); true
|
|
).
|
|
|
|
|
|
|
|
mse_testset :-
|
|
(
|
|
current_predicate(user:test_example/3)
|
|
->
|
|
(
|
|
update_values,
|
|
findall(SquaredError,
|
|
(user:test_example(QueryID,_Query,QueryProb),
|
|
query_probability(QueryID,CurrentProb),
|
|
SquaredError is (CurrentProb-QueryProb)**2),
|
|
AllSquaredErrors),
|
|
|
|
length(AllSquaredErrors,Length),
|
|
sum_list(AllSquaredErrors,SumAllSquaredErrors),
|
|
min_list(AllSquaredErrors,MinError),
|
|
max_list(AllSquaredErrors,MaxError),
|
|
MSE is SumAllSquaredErrors/Length,
|
|
|
|
logger_set_variable(mse_testset,MSE),
|
|
logger_set_variable(mse_min_testset,MinError),
|
|
logger_set_variable(mse_max_testset,MaxError)
|
|
); true
|
|
).
|
|
|
|
|
|
|
|
%========================================================================
|
|
%= Calculates the sigmoid function respectivly the inverse of it
|
|
%= warning: applying inv_sigmoid to 0.0 or 1.0 will yield +/-inf
|
|
%=
|
|
%= +Float, -Float
|
|
%========================================================================
|
|
|
|
sigmoid(T,Sig) :-
|
|
sigmoid_slope(Slope),
|
|
Sig is 1/(1+exp(-T*Slope)).
|
|
|
|
inv_sigmoid(T,InvSig) :-
|
|
sigmoid_slope(Slope),
|
|
InvSig is -log(1/T-1)/Slope.
|
|
|
|
|
|
%========================================================================
|
|
%= this functions truncates probabilities too close to 1.0 or 0.0
|
|
%= the reason is, applying the inverse sigmoid function would yield +/- inf
|
|
%= for such extreme values
|
|
%=
|
|
%= +Float, -Float
|
|
%========================================================================
|
|
|
|
secure_probability(Prob,Prob_Secure) :-
|
|
TMP is max(0.000000001,Prob),
|
|
Prob_Secure is min(0.999999999,TMP).
|
|
|
|
|
|
|
|
%========================================================================
|
|
%= Perform one iteration of gradient descent
|
|
%=
|
|
%= assumes that everything is initialized, if the current values
|
|
%= of query_probability/2 and query_gradient/3 are not up to date
|
|
%= they will be recalculated
|
|
%= finally, the values_correct/0 is retracted to signal that the
|
|
%= probabilities of the examples have to be recalculated
|
|
%========================================================================
|
|
|
|
save_old_probabilities :-
|
|
( % go over all tunable facts
|
|
|
|
tunable_fact(FactID,_),
|
|
get_fact_probability(FactID,OldProbability),
|
|
atomic_concat(['old_prob_',FactID],Key),
|
|
bb_put(Key,OldProbability),
|
|
|
|
fail; % go to next tunable fact
|
|
true
|
|
).
|
|
|
|
forget_old_values :-
|
|
( % go over all tunable facts
|
|
|
|
tunable_fact(FactID,_),
|
|
atomic_concat(['old_prob_',FactID],Key),
|
|
atomic_concat(['grad_',FactID],Key2),
|
|
bb_delete(Key,_),
|
|
bb_delete(Key2,_),
|
|
|
|
fail; % go to next tunable fact
|
|
true
|
|
).
|
|
|
|
add_gradient(Learning_Rate) :-
|
|
( % go over all tunable facts
|
|
|
|
tunable_fact(FactID,_),
|
|
atomic_concat(['old_prob_',FactID],Key),
|
|
atomic_concat(['grad_',FactID],Key2),
|
|
|
|
bb_get(Key,OldProbability),
|
|
bb_get(Key2,GradValue),
|
|
|
|
inv_sigmoid(OldProbability,OldValue),
|
|
NewValue is OldValue -Learning_Rate*GradValue,
|
|
sigmoid(NewValue,NewProbability),
|
|
|
|
% Prevent "inf" by using values too close to 1.0
|
|
secure_probability(NewProbability,NewProbabilityS),
|
|
set_fact_probability(FactID,NewProbabilityS),
|
|
|
|
fail; % go to next tunable fact
|
|
|
|
true
|
|
),
|
|
retractall(values_correct).
|
|
|
|
simulate :-
|
|
L = [0.6,1.0,2.0,3.0,10,50,100,200,300],
|
|
|
|
findall((X,Y),(member(X,L),line_search_evaluate_point(X,Y)),List),
|
|
write(List),nl.
|
|
|
|
gradient_descent :-
|
|
|
|
save_old_probabilities,
|
|
update_values,
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% start set gradient to zero
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
( % go over all tunable facts
|
|
|
|
tunable_fact(FactID,_),
|
|
atomic_concat(['grad_',FactID],Key),
|
|
bb_put(Key,0.0),
|
|
fail; % go to next tunable fact
|
|
|
|
true
|
|
),
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% stop gradient to zero
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
!,
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% start calculate gradient
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
alpha(Alpha),
|
|
example_count(ExampleCount),
|
|
( % go over all training examples
|
|
current_predicate(user:example/3),
|
|
user:example(QueryID,_Query,QueryProb),
|
|
query_probability(QueryID,BDDProb),
|
|
(
|
|
QueryProb=:=0.0
|
|
->
|
|
Y2=Alpha;
|
|
Y2=1.0
|
|
),
|
|
Y is Y2*2/ExampleCount * (BDDProb-QueryProb),
|
|
|
|
|
|
( % go over all tunable facts
|
|
|
|
tunable_fact(FactID,_),
|
|
atomic_concat(['grad_',FactID],Key),
|
|
|
|
% if the following query fails,
|
|
% it means, the fact is not used in the proof
|
|
% of QueryID, and the gradient is 0.0 and will
|
|
% not contribute to NewValue either way
|
|
% DON'T FORGET THIS IF YOU CHANGE SOMETHING HERE!
|
|
query_gradient(QueryID,FactID,GradValue),
|
|
|
|
bb_get(Key,OldValue),
|
|
NewValue is OldValue + Y*GradValue,
|
|
bb_put(Key,NewValue),
|
|
fail; % go to next fact
|
|
|
|
true
|
|
),
|
|
fail; % go to next training example
|
|
|
|
true
|
|
),
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% stop calculate gradient
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
!,
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% start statistics on gradient
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
findall(V,(tunable_fact(FactID,_),atomic_concat(['grad_',FactID],Key),bb_get(Key,V)),GradientValues),
|
|
|
|
sum_list(GradientValues,GradSum),
|
|
max_list(GradientValues,GradMax),
|
|
min_list(GradientValues,GradMin),
|
|
length(GradientValues,GradLength),
|
|
GradMean is GradSum/GradLength,
|
|
|
|
logger_set_variable(gradient_mean,GradMean),
|
|
logger_set_variable(gradient_min,GradMin),
|
|
logger_set_variable(gradient_max,GradMax),
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% stop statistics on gradient
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% start add gradient to current probabilities
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
(
|
|
line_search(false)
|
|
->
|
|
learning_rate(LearningRate);
|
|
lineSearch(LearningRate,_)
|
|
),
|
|
format('learning rate = ~12f~n',[LearningRate]),
|
|
add_gradient(LearningRate),
|
|
logger_set_variable(learning_rate,LearningRate),
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% stop add gradient to current probabilities
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
!,
|
|
forget_old_values.
|
|
|
|
%========================================================================
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
|
|
line_search_evaluate_point(Learning_Rate,MSE) :-
|
|
add_gradient(Learning_Rate),
|
|
mse_trainingset_only_for_linesearch(MSE).
|
|
|
|
|
|
lineSearch(Final_X,Final_Value) :-
|
|
|
|
% Get Parameters for line search
|
|
line_search_tolerance(Tol),
|
|
line_search_tau(Tau),
|
|
line_search_interval(A,B),
|
|
|
|
format(' Running line search in interval (~5f,~5f)~n',[A,B]),
|
|
|
|
% init values
|
|
Acc is Tol * (B-A),
|
|
InitRight is A + Tau*(B-A),
|
|
InitLeft is A + B - InitRight,
|
|
|
|
line_search_evaluate_point(A,Value_A),
|
|
line_search_evaluate_point(B,Value_B),
|
|
line_search_evaluate_point(InitRight,Value_InitRight),
|
|
line_search_evaluate_point(InitLeft,Value_InitLeft),
|
|
|
|
bb_put(line_search_a,A),
|
|
bb_put(line_search_b,B),
|
|
bb_put(line_search_left,InitLeft),
|
|
bb_put(line_search_right,InitRight),
|
|
|
|
bb_put(line_search_value_a,Value_A),
|
|
bb_put(line_search_value_b,Value_B),
|
|
bb_put(line_search_value_left,Value_InitLeft),
|
|
bb_put(line_search_value_right,Value_InitRight),
|
|
|
|
bb_put(line_search_iteration,1),
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
%%%% BEGIN BACK TRACKING
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
(
|
|
repeat,
|
|
|
|
bb_get(line_search_iteration,Iteration),
|
|
bb_get(line_search_a,Ak),
|
|
bb_get(line_search_b,Bk),
|
|
bb_get(line_search_left,Left),
|
|
bb_get(line_search_right,Right),
|
|
|
|
bb_get(line_search_value_a,Fl),
|
|
bb_get(line_search_value_b,Fr),
|
|
bb_get(line_search_value_left,FLeft),
|
|
bb_get(line_search_value_right,FRight),
|
|
|
|
write(lineSearch(Iteration,Ak,Fl,Bk,Fr,Left,FLeft,Right,FRight)),nl,
|
|
|
|
(
|
|
% check for infinity, if there is, go to the left
|
|
( FLeft >= FRight, \+ FLeft = (+inf), \+ FRight = (+inf) )
|
|
->
|
|
(
|
|
AkNew=Left,
|
|
FlNew=FLeft,
|
|
LeftNew=Right,
|
|
FLeftNew=FRight,
|
|
RightNew is AkNew + Bk - LeftNew,
|
|
line_search_evaluate_point(RightNew,FRightNew),
|
|
BkNew=Bk,
|
|
FrNew=Fr
|
|
);
|
|
(
|
|
BkNew=Right,
|
|
FrNew=FRight,
|
|
RightNew=Left,
|
|
FRightNew=FLeft,
|
|
LeftNew is Ak + BkNew - RightNew,
|
|
|
|
line_search_evaluate_point(LeftNew,FLeftNew),
|
|
AkNew=Ak,
|
|
FlNew=Fl
|
|
)
|
|
),
|
|
|
|
Next_Iteration is Iteration + 1,
|
|
|
|
ActAcc is BkNew -AkNew,
|
|
|
|
bb_put(line_search_iteration,Next_Iteration),
|
|
|
|
bb_put(line_search_a,AkNew),
|
|
bb_put(line_search_b,BkNew),
|
|
bb_put(line_search_left,LeftNew),
|
|
bb_put(line_search_right,RightNew),
|
|
|
|
bb_put(line_search_value_a,FlNew),
|
|
bb_put(line_search_value_b,FrNew),
|
|
bb_put(line_search_value_left,FLeftNew),
|
|
bb_put(line_search_value_right,FRightNew),
|
|
|
|
% is the search interval smaller than the tolerance level?
|
|
ActAcc < Acc,
|
|
|
|
% apperantly it is, so get me out of here and
|
|
% cut away the choice point from repeat
|
|
!
|
|
),
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
%%%% END BACK TRACKING
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
|
|
% clean up the blackboard mess
|
|
bb_delete(line_search_iteration,_),
|
|
bb_delete(line_search_a,_),
|
|
bb_delete(line_search_b,_),
|
|
bb_delete(line_search_left,_),
|
|
bb_delete(line_search_right,_),
|
|
bb_delete(line_search_value_a,_),
|
|
bb_delete(line_search_value_b,_),
|
|
bb_delete(line_search_value_left,_),
|
|
bb_delete(line_search_value_right,_),
|
|
|
|
% it doesn't harm to check also the value in the middle
|
|
% of the current search interval
|
|
Middle is (AkNew + BkNew) / 2.0,
|
|
line_search_evaluate_point(Middle,Value_Middle),
|
|
|
|
% return the optimal value
|
|
my_5_min(Value_Middle,FlNew,FrNew,FLeftNew,FRightNew,
|
|
Middle,AkNew,BkNew,LeftNew,RightNew,
|
|
Optimal_Value,Optimal_X),
|
|
|
|
line_search_postcheck(Optimal_Value,Optimal_X,Final_Value,Final_X).
|
|
|
|
line_search_postcheck(V,X,V,X) :-
|
|
X>0,
|
|
!.
|
|
line_search_postcheck(V,X,V,X) :-
|
|
line_search_never_stop(false),
|
|
!.
|
|
line_search_postcheck(_,_, LLH, FinalPosition) :-
|
|
line_search_tolerance(Tolerance),
|
|
line_search_interval(Left,Right),
|
|
|
|
|
|
Offset is (Right - Left) * Tolerance,
|
|
|
|
bb_put(line_search_offset,Offset),
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
(
|
|
|
|
|
|
repeat,
|
|
|
|
bb_get(line_search_offset,OldOffset),
|
|
NewOffset is OldOffset * Tolerance,
|
|
bb_put(line_search_offset,NewOffset),
|
|
|
|
Position is Left + NewOffset,
|
|
set_linesearch_weights_calc_llh(Position,LLH),
|
|
bb_put(line_search_llh,LLH),
|
|
|
|
write(logAtom(lineSearchPostCheck(Position,LLH))),nl,
|
|
|
|
|
|
\+ LLH = (+inf),
|
|
!
|
|
), % cut away choice point from repeat
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
|
|
bb_delete(line_search_llh,LLH),
|
|
bb_delete(line_search_offset,FinalOffset),
|
|
FinalPosition is Left + FinalOffset.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
my_5_min(V1,V2,V3,V4,V5,F1,F2,F3,F4,F5,VMin,FMin) :-
|
|
(
|
|
V1<V2
|
|
->
|
|
(VTemp1=V1,FTemp1=F1);
|
|
(VTemp1=V2,FTemp1=F2)
|
|
),
|
|
(
|
|
V3<V4
|
|
->
|
|
(VTemp2=V3,FTemp2=F3);
|
|
(VTemp2=V4,FTemp2=F4)
|
|
),
|
|
(
|
|
VTemp1<VTemp2
|
|
->
|
|
(VTemp3=VTemp1,FTemp3=FTemp1);
|
|
(VTemp3=VTemp2,FTemp3=FTemp2)
|
|
),
|
|
(
|
|
VTemp3<V5
|
|
->
|
|
(VMin=VTemp3,FMin=FTemp3);
|
|
(VMin=V5,FMin=F5)
|
|
).
|
|
|
|
|
|
|
|
%========================================================================
|
|
%= initialize the logger module and set the flags for learning
|
|
%=
|
|
%========================================================================
|
|
|
|
|
|
global_initialize :-
|
|
set_learning_flag(output_directory,'./output'),
|
|
set_learning_flag(query_directory,'./queries'),
|
|
set_learning_flag(log_frequency,5),
|
|
set_learning_flag(rebuild_bdds,false),
|
|
set_learning_flag(rebuild_bdds_it,1),
|
|
set_learning_flag(reuse_initialized_bdds,false),
|
|
set_learning_flag(learning_rate,examples),
|
|
set_learning_flag(check_duplicate_bdds,true),
|
|
set_learning_flag(probability_initializer,(FactID,P,random_probability(FactID,P))),
|
|
set_learning_flag(alpha,1.0),
|
|
set_learning_flag(sigmoid_slope,1.0), % 1.0 gives standard sigmoid
|
|
set_learning_flag(init_method,(Query,Probability,BDDFile,ProbFile,
|
|
problog_kbest_save(Query,100,Probability,_Status,BDDFile,ProbFile))),
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|
set_learning_flag(line_search,false),
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|
set_learning_flag(line_search_never_stop,true),
|
|
set_learning_flag(line_search_tau,0.618033988749895),
|
|
set_learning_flag(line_search_tolerance,0.05),
|
|
set_learning_flag(line_search_interval,(0,100)),
|
|
|
|
logger_define_variable(iteration, int),
|
|
logger_define_variable(duration,time),
|
|
logger_define_variable(mse_trainingset,float),
|
|
logger_define_variable(mse_min_trainingset,float),
|
|
logger_define_variable(mse_max_trainingset,float),
|
|
logger_define_variable(mse_testset,float),
|
|
logger_define_variable(mse_min_testset,float),
|
|
logger_define_variable(mse_max_testset,float),
|
|
logger_define_variable(gradient_mean,float),
|
|
logger_define_variable(gradient_min,float),
|
|
logger_define_variable(gradient_max,float),
|
|
logger_define_variable(ground_truth_diff,float),
|
|
logger_define_variable(ground_truth_mindiff,float),
|
|
logger_define_variable(ground_truth_maxdiff,float),
|
|
logger_define_variable(learning_rate,float).
|
|
|
|
%========================================================================
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
|
|
:- initialization(global_initialize).
|