1721 lines
52 KiB
Prolog
1721 lines
52 KiB
Prolog
%%% -*- Mode: Prolog; -*-
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%
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% $Date: 2010-08-25 15:45:22 +0200 (Wed, 25 Aug 2010) $
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% $Revision: 4692 $
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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 2008, 2009, 2010
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% Katholieke Universiteit Leuven
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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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% 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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% keeping the Package available as open source and free software.
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% General Provisions
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% ensure that your Modified Version complies with the requirements of
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% this license.
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% (12) This license does not grant you the right to use any trademark,
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% (13) This license includes the non-exclusive, worldwide,
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% free-of-charge patent license to make, have made, use, offer to sell,
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% infringement, then this Artistic License to you shall terminate on the
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% date that such litigation is filed.
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% (14) Disclaimer of Warranty: THE PACKAGE IS PROVIDED BY THE COPYRIGHT
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% WARRANTIES. THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
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% PERMITTED BY YOUR LOCAL LAW. UNLESS REQUIRED BY LAW, NO COPYRIGHT
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% INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING IN ANY WAY OUT OF THE USE
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% OF THE PACKAGE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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%
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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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learning_flag/2,
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learning_flags/0,
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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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auto_alpha/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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:- ensure_loaded(library(lists)).
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:- ensure_loaded(library(random)).
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:- ensure_loaded(library(system)).
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% load our own modules
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:- ensure_loaded(problog).
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:- ensure_loaded('problog/logger').
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:- ensure_loaded('problog/flags').
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:- ensure_loaded('problog/os').
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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/4.
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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_md5/3.
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:- assert_static(user:(example(A,B,C,=) :- current_predicate(example/3), example(A,B,C))).
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:- assert_static(user:(test_example(A,B,C,=) :- current_predicate(test_example/3), test_example(A,B,C))).
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%========================================================================
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%=
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%=
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%=
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%========================================================================
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my_format(Level,String,Arguments) :-
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problog_flag(learning_verbosity_level,V_Level),
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(
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V_Level >= Level
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->
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(format(String,Arguments),flush_output(user));
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true
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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:-
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current_iteration(Iteration),
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atomic_concat(['factprobs_',Iteration,'.pl'],Filename),
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problog_flag(output_directory,Dir),
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concat_path_with_filename(Dir,Filename,Filename2),
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export_facts(Filename2).
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%========================================================================
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%= store the current succes probabilities for training and test examples
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%=
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%========================================================================
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save_predictions:-
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current_iteration(Iteration),
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atomic_concat(['predictions_',Iteration,'.pl'],Filename),
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problog_flag(output_directory,Dir),
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concat_path_with_filename(Dir,Filename,Filename2),
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open(Filename2,'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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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% start save prediction test examples
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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( % go over all test examples
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current_predicate(user:test_example/4),
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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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!,
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% stop save prediction test examples
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% start save prediction training examples
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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( % go over all training examples
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current_predicate(user:example/4),
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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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!,
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% stop save prediction training examples
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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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%= 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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check_examples :-
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Check example IDs
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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(
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(current_predicate(user:example/4),user:example(ID,_,_,_), \+ atomic(ID))
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->
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(
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format(user_error,'The example id of training example ~q ',[ID]),
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format(user_error,'is not atomic (e.g foo42, 23, bar, ...).~n',[]),
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throw(error(examples))
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); true
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),
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(
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(current_predicate(user:test_example/4),user:test_example(ID,_,_,_), \+ atomic(ID))
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->
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(
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format(user_error,'The example id of test example ~q ',[ID]),
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format(user_error,'is not atomic (e.g foo42, 23, bar, ...).~n',[]),
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throw(error(examples))
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); true
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),
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Check example probabilities
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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(
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(current_predicate(user:example/4),user:example(ID,_,P,_), (\+ number(P); P>1 ; P<0))
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->
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(
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format(user_error,'The training example ~q does not have a valid probaility value (~q).~n',[ID,P]),
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throw(error(examples))
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); true
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),
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(
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(current_predicate(user:test_example/4),user:test_example(ID,_,P,_), (\+ number(P); P>1 ; P<0))
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->
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(
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format(user_error,'The test example ~q does not have a valid probaility value (~q).~n',[ID,P]),
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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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% Check that no example ID is repeated,
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% and if it is repeated make sure the query is the same
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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(
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(
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(
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current_predicate(user:example/4),
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user:example(ID,QueryA,_,_),
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user:example(ID,QueryB,_,_),
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QueryA \= QueryB
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) ;
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(
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current_predicate(user:test_example/4),
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user:test_example(ID,QueryA,_,_),
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user:test_example(ID,QueryB,_,_),
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QueryA \= QueryB
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);
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(
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current_predicate(user:example/4),
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current_predicate(user:test_example/4),
|
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user:example(ID,QueryA,_,_),
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user:test_example(ID,QueryB,_,_),
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QueryA \= QueryB
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)
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)
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->
|
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(
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format(user_error,'The example id ~q is used several times.~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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%= initialize everything and perform Iterations times gradient descent
|
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%= can be called several times
|
|
%= if it is called with an epsilon parameter, it stops when the change
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%= in the MSE is smaller than epsilon
|
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%========================================================================
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do_learning(Iterations) :-
|
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do_learning(Iterations,-1).
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|
|
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do_learning(Iterations,Epsilon) :-
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current_predicate(user:example/4),
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!,
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integer(Iterations),
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number(Epsilon),
|
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Iterations>0,
|
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do_learning_intern(Iterations,Epsilon).
|
|
do_learning(_,_) :-
|
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format(user_error,'~n~nWarning: No training examples specified !!!~n~n',[]).
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|
|
|
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do_learning_intern(0,_) :-
|
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!.
|
|
do_learning_intern(Iterations,Epsilon) :-
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Iterations>0,
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|
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init_learning,
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|
current_iteration(CurrentIteration),
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!,
|
|
retractall(current_iteration(_)),
|
|
!,
|
|
NextIteration is CurrentIteration+1,
|
|
assert(current_iteration(NextIteration)),
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EndIteration is CurrentIteration+Iterations-1,
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|
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my_format(1,'~nIteration ~d of ~d~n',[CurrentIteration,EndIteration]),
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logger_set_variable(iteration,CurrentIteration),
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|
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logger_start_timer(duration),
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|
|
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mse_testset,
|
|
once(ground_truth_difference),
|
|
gradient_descent,
|
|
|
|
problog_flag(log_frequency,Log_Frequency),
|
|
|
|
(
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|
( Log_Frequency>0, 0 =:= CurrentIteration mod Log_Frequency)
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|
->
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(
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once(save_predictions),
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|
once(save_model)
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);
|
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true
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),
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|
|
|
update_values,
|
|
|
|
(
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last_mse(Last_MSE)
|
|
->
|
|
(
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retractall(last_mse(_)),
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|
logger_get_variable(mse_trainingset,Current_MSE),
|
|
assert(last_mse(Current_MSE)),
|
|
!,
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|
MSE_Diff is abs(Last_MSE-Current_MSE)
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|
); (
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|
logger_get_variable(mse_trainingset,Current_MSE),
|
|
assert(last_mse(Current_MSE)),
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|
MSE_Diff is Epsilon+1
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|
)
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|
),
|
|
|
|
(
|
|
(problog_flag(rebuild_bdds,BDDFreq),BDDFreq>0,0 =:= CurrentIteration mod BDDFreq)
|
|
->
|
|
(
|
|
retractall(values_correct),
|
|
once(delete_all_queries),
|
|
once(init_queries)
|
|
); true
|
|
),
|
|
|
|
|
|
!,
|
|
logger_stop_timer(duration),
|
|
|
|
|
|
logger_write_data,
|
|
|
|
|
|
|
|
RemainingIterations is Iterations-1,
|
|
|
|
(
|
|
MSE_Diff>Epsilon
|
|
->
|
|
do_learning_intern(RemainingIterations,Epsilon);
|
|
true
|
|
).
|
|
|
|
|
|
%========================================================================
|
|
%= find proofs and build bdds for all training and test examples
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
init_learning :-
|
|
learning_initialized,
|
|
!.
|
|
init_learning :-
|
|
check_examples,
|
|
|
|
logger_write_header,
|
|
|
|
my_format(1,'Initializing everything~n',[]),
|
|
empty_output_directory,
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% Delete the BDDs from the previous run if they should
|
|
% not be reused
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
(
|
|
(
|
|
problog_flag(reuse_initialized_bdds,true),
|
|
problog_flag(rebuild_bdds,0)
|
|
)
|
|
->
|
|
true;
|
|
delete_all_queries
|
|
),
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% Check, if continuous facts are used.
|
|
% if yes, switch to problog_exact
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
problog_flag(init_method,(_,_,_,_,OldCall)),
|
|
(
|
|
(
|
|
continuous_fact(_),
|
|
OldCall\=problog_exact_save(_,_,_,_,_)
|
|
)
|
|
->
|
|
(
|
|
format('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
|
|
),
|
|
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% start count test examples
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
bb_put(test_examples,0),
|
|
( % go over all test examples
|
|
current_predicate(user:test_example/4),
|
|
user:test_example(_,_,_,_),
|
|
bb_get(test_examples, OldCounter),
|
|
NewCounter is OldCounter+1,
|
|
bb_put(test_examples,NewCounter),
|
|
|
|
fail; % go to next text example
|
|
true
|
|
),
|
|
bb_delete(test_examples,TestExampleCount),
|
|
my_format(3,'~q test examples~n',[TestExampleCount]),
|
|
!,
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% stop count test examples
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% start count training examples
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
bb_put(training_examples,0),
|
|
( % go over all training examples
|
|
current_predicate(user:example/4),
|
|
user:example(_,_,_,_),
|
|
bb_get(training_examples, OldCounter),
|
|
NewCounter is OldCounter+1,
|
|
bb_put(training_examples,NewCounter),
|
|
|
|
fail; %go to next training example
|
|
true
|
|
),
|
|
bb_delete(training_examples,TrainingExampleCount),
|
|
assert(example_count(TrainingExampleCount)),
|
|
my_format(3,'~q training examples~n',[TrainingExampleCount]),
|
|
!,
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% stop count training examples
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% set learning rate and alpha
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
(
|
|
problog_flag(learning_rate,examples)
|
|
->
|
|
set_problog_flag(learning_rate,TrainingExampleCount);
|
|
true
|
|
),
|
|
|
|
(
|
|
problog_flag(alpha,auto)
|
|
->
|
|
auto_alpha;
|
|
true
|
|
),
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% build BDD script for every example
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
once(initialize_fact_probabilities),
|
|
once(init_queries),
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% done
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
assert(current_iteration(0)),
|
|
assert(learning_initialized),
|
|
|
|
my_format(1,'~n',[]).
|
|
|
|
|
|
%========================================================================
|
|
%=
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
|
|
|
|
|
|
delete_all_queries :-
|
|
problog_flag(bdd_directory,BDD_Directory),
|
|
empty_bdd_directory(BDD_Directory),
|
|
retractall(query_is_similar(_,_)),
|
|
retractall(query_md5(_,_,_)).
|
|
|
|
empty_output_directory :-
|
|
problog_flag(output_directory,Directory),
|
|
empty_output_directory(Directory).
|
|
|
|
%========================================================================
|
|
%= This predicate goes over all training and test examples,
|
|
%= calls the inference method of ProbLog and stores the resulting
|
|
%= BDDs
|
|
%========================================================================
|
|
|
|
|
|
init_queries :-
|
|
my_format(2,'Build BDDs for examples~n',[]),
|
|
( % go over all test examples
|
|
current_predicate(user:test_example/4),
|
|
user:test_example(ID,Query,Prob,_),
|
|
my_format(3,' test example ~q: ~q~n',[ID,Query]),
|
|
flush_output(user),
|
|
init_one_query(ID,Query,test),
|
|
|
|
fail; % go to next test example
|
|
true
|
|
),
|
|
( % go over all training examples
|
|
current_predicate(user:example/4),
|
|
user:example(ID,Query,Prob,_),
|
|
my_format(3,' training example ~q: ~q~n',[ID,Query]),
|
|
flush_output(user),
|
|
init_one_query(ID,Query,training),
|
|
|
|
fail; %go to next training example
|
|
true
|
|
).
|
|
|
|
|
|
bdd_input_file(Filename) :-
|
|
problog_flag(output_directory,Dir),
|
|
concat_path_with_filename(Dir,'input.txt',Filename).
|
|
|
|
init_one_query(QueryID,Query,Type) :-
|
|
bdd_input_file(Probabilities_File),
|
|
problog_flag(bdd_directory,Query_Directory),
|
|
|
|
atomic_concat(['query_',QueryID],Filename1),
|
|
concat_path_with_filename(Query_Directory,Filename1,Filename),
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% if BDD file does not exist, call ProbLog
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
(
|
|
file_exists(Filename)
|
|
->
|
|
my_format(3,' Reuse existing BDD ~q~n~n',[Filename]);
|
|
(
|
|
problog_flag(init_method,(Query,_Prob,Filename,Probabilities_File,Call)),
|
|
once(call(Call)),
|
|
delete_file(Probabilities_File)
|
|
)
|
|
),
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% check wether this BDD is similar to another BDD
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
(
|
|
problog_flag(check_duplicate_bdds,true)
|
|
->
|
|
(
|
|
calc_md5(Filename,Query_MD5),
|
|
(
|
|
query_md5(OtherQueryID,Query_MD5,Type)
|
|
->
|
|
(
|
|
assert(query_is_similar(QueryID,OtherQueryID)),
|
|
my_format(3, '~q is similar to ~q~2n', [QueryID,OtherQueryID])
|
|
);
|
|
assert(query_md5(QueryID,Query_MD5,Type))
|
|
)
|
|
);
|
|
|
|
true
|
|
).
|
|
|
|
|
|
%========================================================================
|
|
%= set all unknown fact probabilities to random values
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
|
|
initialize_fact_probabilities :-
|
|
( % go over all tunable facts
|
|
tunable_fact(FactID,_),
|
|
problog_flag(probability_initializer,(FactID,Probability,Query)),
|
|
once(call(Query)),
|
|
set_fact_probability(FactID,Probability),
|
|
|
|
fail; % go to next tunable fact
|
|
true
|
|
).
|
|
|
|
random_probability(_FactID,Probability) :-
|
|
% use probs around 0.5 to not confuse k-best search
|
|
random(Random),
|
|
Probability is 0.5+(Random-0.5)/100.
|
|
|
|
|
|
|
|
%========================================================================
|
|
%= updates all values of query_probability/2 and query_gradient/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
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
once(retractall(query_probability_intern(_,_))),
|
|
once(retractall(query_gradient_intern(_,_,_,_))),
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% start write current probabilities to file
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
bdd_input_file(Probabilities_File),
|
|
(
|
|
file_exists(Probabilities_File)
|
|
->
|
|
delete_file(Probabilities_File);
|
|
true
|
|
),
|
|
|
|
open(Probabilities_File,'write',Handle),
|
|
|
|
( % go over all probabilistic facts
|
|
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
|
|
),
|
|
|
|
( % go over all continuous facts
|
|
get_continuous_fact_parameters(ID,gaussian(Mu,Sigma)),
|
|
%SigmaL is log(Sigma),
|
|
SigmaL=Sigma,
|
|
format(Handle,'@x~q_*~n0~n0~n~10f;~10f~n',[ID,Mu,SigmaL]),
|
|
|
|
fail; % go to next continuous fact
|
|
true
|
|
),
|
|
|
|
close(Handle),
|
|
!,
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% stop write current probabilities to file
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
|
|
assert(values_correct).
|
|
|
|
|
|
|
|
%========================================================================
|
|
%=
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
|
|
update_query_cleanup(QueryID) :-
|
|
(
|
|
(query_is_similar(QueryID,_) ; query_is_similar(_,QueryID))
|
|
->
|
|
% either this query is similar to another or vice versa,
|
|
% therefore we don't delete anything
|
|
true;
|
|
once(retractall(query_gradient_intern(QueryID,_,_,_)))
|
|
).
|
|
|
|
|
|
update_query(QueryID,Symbol,What_To_Update) :-
|
|
% fixme OS trouble
|
|
problog_flag(output_directory,Output_Directory),
|
|
problog_flag(bdd_directory,Query_Directory),
|
|
bdd_input_file(Probabilities_File),
|
|
(
|
|
query_is_similar(QueryID,_)
|
|
->
|
|
% we don't have to evaluate the BDD
|
|
my_format(4,'#',[]);
|
|
(
|
|
problog_flag(sigmoid_slope,Slope),
|
|
problog_dir(PD),
|
|
((What_To_Update=all;query_is_similar(_,QueryID)) -> Method='g' ; Method='l'),
|
|
atomic_concat([PD,
|
|
'/problogbdd',
|
|
' -i "', Probabilities_File, '"',
|
|
' -l "', Query_Directory,'/query_',QueryID, '"',
|
|
' -m ', Method,
|
|
' -id ', QueryID,
|
|
' -sl ', Slope,
|
|
' > "',
|
|
Output_Directory,
|
|
'values.pl"'],Command),
|
|
shell(Command,Error),
|
|
|
|
|
|
(
|
|
Error = 2
|
|
->
|
|
throw(error('SimpleCUDD has been interrupted.'));
|
|
true
|
|
),
|
|
(
|
|
Error \= 0
|
|
->
|
|
(
|
|
format(user_error,'SimpleCUDD stopped with error code ~q, command was ~q~n',[Error, shell(Command,Error)]),
|
|
throw(bdd_error(QueryID,Error)));
|
|
true
|
|
),
|
|
atomic_concat([Output_Directory,'values.pl'],Values_Filename),
|
|
(
|
|
file_exists(Values_Filename)
|
|
->
|
|
(
|
|
(
|
|
once(my_load(Values_Filename,QueryID))
|
|
->
|
|
true;
|
|
(
|
|
format(user_error,'ERROR: Tried to read the file ~q but my_load/1 fails.~n~q.~2n',[Values_Filename,update_query(QueryID,Symbol,What_To_Update)]),
|
|
throw(error(my_load_fails))
|
|
)
|
|
);
|
|
(
|
|
format(user_error,'ERROR: Tried to read the file ~q but it does not exist.~n~q.~2n',[Values_Filename,update_query(QueryID,Symbol,What_To_Update)]),
|
|
throw(error(output_file_does_not_exist))
|
|
)
|
|
)
|
|
),
|
|
|
|
delete_file(Values_Filename),
|
|
my_format(4,'~w',[Symbol])
|
|
)
|
|
),
|
|
flush_output(user).
|
|
|
|
|
|
%========================================================================
|
|
%= This predicate reads probability and gradient values from the file
|
|
%= the gradient ID is a mere check to uncover hidden bugs
|
|
%= +Filename +QueryID -QueryProbability
|
|
%========================================================================
|
|
|
|
my_load(File,QueryID) :-
|
|
open(File,'read',Handle),
|
|
read(Handle,Atom),
|
|
once(my_load_intern(Atom,Handle,QueryID)),
|
|
close(Handle).
|
|
my_load(File,QueryID) :-
|
|
format(user_error,'Error at ~q.~2n',[my_load(File,QueryID)]),
|
|
throw(error(my_load(File,QueryID))).
|
|
|
|
my_load_intern(end_of_file,_,_) :-
|
|
!.
|
|
my_load_intern(query_probability(QueryID,Prob),Handle,QueryID) :-
|
|
!,
|
|
assert(query_probability_intern(QueryID,Prob)),
|
|
read(Handle,X),
|
|
my_load_intern(X,Handle,QueryID).
|
|
my_load_intern(query_gradient(QueryID,XFactID,Type,Value),Handle,QueryID) :-
|
|
!,
|
|
atomic_concat(x,StringFactID,XFactID),
|
|
atom_number(StringFactID,FactID),
|
|
assert(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) :-
|
|
(
|
|
current_predicate(user:example/4)
|
|
->
|
|
(
|
|
update_values,
|
|
findall(SquaredError,
|
|
(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<QueryProb))
|
|
->
|
|
SquaredError is (CurrentProb-QueryProb)**2;
|
|
SquaredError = 0.0
|
|
)
|
|
),
|
|
|
|
AllSquaredErrors),
|
|
|
|
length(AllSquaredErrors,Length),
|
|
sum_list(AllSquaredErrors,SumAllSquaredErrors),
|
|
MSE is SumAllSquaredErrors/Length,
|
|
my_format(3,' (~8f)~n',[MSE])
|
|
); true
|
|
),
|
|
retractall(values_correct).
|
|
|
|
mse_testset :-
|
|
(
|
|
(current_predicate(user:test_example/4),user:test_example(_,_,_,_))
|
|
->
|
|
(
|
|
my_format(2,'MSE_Test ',[]),
|
|
update_values,
|
|
findall(SquaredError,
|
|
(user:test_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<QueryProb))
|
|
->
|
|
SquaredError is (CurrentProb-QueryProb)**2;
|
|
SquaredError = 0.0
|
|
)
|
|
),
|
|
AllSquaredErrors),
|
|
|
|
length(AllSquaredErrors,Length),
|
|
sum_list(AllSquaredErrors,SumAllSquaredErrors),
|
|
min_list(AllSquaredErrors,MinError),
|
|
max_list(AllSquaredErrors,MaxError),
|
|
MSE is SumAllSquaredErrors/Length,
|
|
|
|
logger_set_variable(mse_testset,MSE),
|
|
logger_set_variable(mse_min_testset,MinError),
|
|
logger_set_variable(mse_max_testset,MaxError),
|
|
my_format(2,' (~8f)~n',[MSE])
|
|
); true
|
|
).
|
|
|
|
%========================================================================
|
|
%= Calculates the sigmoid function respectivly the inverse of it
|
|
%= warning: applying inv_sigmoid to 0.0 or 1.0 will yield +/-inf
|
|
%=
|
|
%= +Float, -Float
|
|
%========================================================================
|
|
|
|
sigmoid(T,Sig) :-
|
|
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 :-
|
|
( % go over all tunable facts
|
|
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)
|
|
)
|
|
),
|
|
|
|
fail; % go to next tunable fact
|
|
true
|
|
).
|
|
|
|
|
|
|
|
forget_old_probabilities :-
|
|
( % go over all tunable facts
|
|
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,_)
|
|
)
|
|
),
|
|
|
|
fail; % go to next tunable fact
|
|
true
|
|
).
|
|
|
|
add_gradient(Learning_Rate) :-
|
|
( % go over all tunable facts
|
|
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)
|
|
)
|
|
),
|
|
|
|
fail; % go to next tunable fact
|
|
true
|
|
),
|
|
retractall(values_correct).
|
|
|
|
|
|
gradient_descent :-
|
|
my_format(2,'Gradient ',[]),
|
|
|
|
save_old_probabilities,
|
|
update_values,
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% start set gradient to zero
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
( % go over all tunable facts
|
|
|
|
tunable_fact(FactID,_),
|
|
(
|
|
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)
|
|
)
|
|
),
|
|
|
|
fail; % go to next tunable fact
|
|
|
|
true
|
|
),
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% stop gradient to zero
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
!,
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% start calculate gradient
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
bb_put(mse_train_sum, 0.0),
|
|
bb_put(mse_train_min, 0.0),
|
|
bb_put(mse_train_max, 0.0),
|
|
|
|
problog_flag(alpha,Alpha),
|
|
logger_set_variable(alpha,Alpha),
|
|
example_count(Example_Count),
|
|
|
|
( % go over all training examples
|
|
current_predicate(user:example/4),
|
|
user:example(QueryID,_Query,QueryProb,Type),
|
|
once(update_query(QueryID,'.',all)),
|
|
query_probability(QueryID,BDDProb),
|
|
(
|
|
QueryProb=:=0.0
|
|
->
|
|
Y2=Alpha;
|
|
Y2=1.0
|
|
),
|
|
(
|
|
(Type == '='; (Type == '<', BDDProb>QueryProb); (Type=='>',BDDProb<QueryProb))
|
|
->
|
|
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<QueryProb))
|
|
->
|
|
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),
|
|
New_MSE_Train_Sum is Old_MSE_Train_Sum+Squared_Error,
|
|
New_MSE_Train_Min is min(Old_MSE_Train_Min,Squared_Error),
|
|
New_MSE_Train_Max is max(Old_MSE_Train_Max,Squared_Error),
|
|
bb_put(mse_train_sum,New_MSE_Train_Sum),
|
|
bb_put(mse_train_min,New_MSE_Train_Min),
|
|
bb_put(mse_train_max,New_MSE_Train_Max),
|
|
|
|
|
|
|
|
( % go over all tunable facts
|
|
tunable_fact(FactID,_),
|
|
(
|
|
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)),
|
|
fail; % go to next training example
|
|
true
|
|
),
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% stop calculate gradient
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
!,
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% start statistics on gradient
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
findall(V, (
|
|
tunable_fact(FactID,_),
|
|
atomic_concat(['grad_',FactID],Key),
|
|
bb_get(Key,V)
|
|
),Gradient_Values),
|
|
|
|
(
|
|
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),
|
|
MSE is MSE_Train_Sum/Example_Count,
|
|
|
|
logger_set_variable(mse_trainingset,MSE),
|
|
logger_set_variable(mse_min_trainingset,MSE_Train_Min),
|
|
logger_set_variable(mse_max_trainingset,MSE_Train_Max),
|
|
|
|
my_format(2,'~n',[]),
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% start add gradient to current probabilities
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
(
|
|
problog_flag(line_search,false)
|
|
->
|
|
problog_flag(learning_rate,LearningRate);
|
|
lineSearch(LearningRate,_)
|
|
),
|
|
my_format(3,'learning rate:~8f~n',[LearningRate]),
|
|
add_gradient(LearningRate),
|
|
logger_set_variable(learning_rate,LearningRate),
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% stop add gradient to current probabilities
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
!,
|
|
forget_old_probabilities.
|
|
|
|
%========================================================================
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
|
|
line_search_evaluate_point(Learning_Rate,MSE) :-
|
|
add_gradient(Learning_Rate),
|
|
my_format(2,'Line search (h=~8f) ',[Learning_Rate]),
|
|
mse_trainingset_only_for_linesearch(MSE).
|
|
|
|
|
|
lineSearch(Final_X,Final_Value) :-
|
|
|
|
% Get Parameters for line search
|
|
problog_flag(line_search_tolerance,Tol),
|
|
problog_flag(line_search_tau,Tau),
|
|
problog_flag(line_search_interval,(A,B)),
|
|
|
|
my_format(3,'Line search in interval (~4f,~4f)~n',[A,B]),
|
|
|
|
% init values
|
|
Acc is Tol * (B-A),
|
|
InitRight is A + Tau*(B-A),
|
|
InitLeft is A + B - InitRight,
|
|
|
|
line_search_evaluate_point(A,Value_A),
|
|
line_search_evaluate_point(B,Value_B),
|
|
line_search_evaluate_point(InitRight,Value_InitRight),
|
|
line_search_evaluate_point(InitLeft,Value_InitLeft),
|
|
|
|
bb_put(line_search_a,A),
|
|
bb_put(line_search_b,B),
|
|
bb_put(line_search_left,InitLeft),
|
|
bb_put(line_search_right,InitRight),
|
|
|
|
bb_put(line_search_value_a,Value_A),
|
|
bb_put(line_search_value_b,Value_B),
|
|
bb_put(line_search_value_left,Value_InitLeft),
|
|
bb_put(line_search_value_right,Value_InitRight),
|
|
|
|
bb_put(line_search_iteration,1),
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
%%%% BEGIN BACK TRACKING
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
(
|
|
repeat,
|
|
|
|
bb_get(line_search_iteration,Iteration),
|
|
bb_get(line_search_a,Ak),
|
|
bb_get(line_search_b,Bk),
|
|
bb_get(line_search_left,Left),
|
|
bb_get(line_search_right,Right),
|
|
|
|
bb_get(line_search_value_a,Fl),
|
|
bb_get(line_search_value_b,Fr),
|
|
bb_get(line_search_value_left,FLeft),
|
|
bb_get(line_search_value_right,FRight),
|
|
|
|
(
|
|
% check for infinity, if there is, go to the left
|
|
( FLeft >= FRight, \+ FLeft = (+inf), \+ FRight = (+inf) )
|
|
->
|
|
(
|
|
AkNew=Left,
|
|
FlNew=FLeft,
|
|
LeftNew=Right,
|
|
FLeftNew=FRight,
|
|
RightNew is AkNew + Bk - LeftNew,
|
|
line_search_evaluate_point(RightNew,FRightNew),
|
|
BkNew=Bk,
|
|
FrNew=Fr
|
|
);
|
|
(
|
|
BkNew=Right,
|
|
FrNew=FRight,
|
|
RightNew=Left,
|
|
FRightNew=FLeft,
|
|
LeftNew is Ak + BkNew - RightNew,
|
|
|
|
line_search_evaluate_point(LeftNew,FLeftNew),
|
|
AkNew=Ak,
|
|
FlNew=Fl
|
|
)
|
|
),
|
|
|
|
Next_Iteration is Iteration + 1,
|
|
|
|
bb_put(line_search_iteration,Next_Iteration),
|
|
|
|
bb_put(line_search_a,AkNew),
|
|
bb_put(line_search_b,BkNew),
|
|
bb_put(line_search_left,LeftNew),
|
|
bb_put(line_search_right,RightNew),
|
|
|
|
bb_put(line_search_value_a,FlNew),
|
|
bb_put(line_search_value_b,FrNew),
|
|
bb_put(line_search_value_left,FLeftNew),
|
|
bb_put(line_search_value_right,FRightNew),
|
|
|
|
% is the search interval smaller than the tolerance level?
|
|
BkNew-AkNew<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) :-
|
|
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<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)
|
|
).
|
|
|
|
|
|
%========================================================================
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%= set the alpha parameter to the value
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%= # positive training examples / # negative training examples
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%=
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%= training example is positive if P(e)=1
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%= training example is negative if P(e)=0
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%=
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%= if there are training example with 0<P<1, set alpha=1.0
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%========================================================================
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auto_alpha :-
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\+ current_predicate(user:example/4),
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!,
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set_problog_flag(alpha,1.0).
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auto_alpha :-
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user:example(_,_,P,_),
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P<1,
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P>0,
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!,
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set_problog_flag(alpha,1.0).
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auto_alpha :-
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findall(1,(user:example(_,_,P,=),P=:=1.0),Pos),
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findall(0,(user:example(_,_,P,=),P=:=0.0),Neg),
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length(Pos,NP),
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length(Neg,NN),
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Alpha is NP/NN,
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set_problog_flag(alpha,Alpha).
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%========================================================================
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%= initialize the logger module and set the flags for learning
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%= don't change anything here! use set_learning_flag/2 instead
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%========================================================================
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init_flags :-
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prolog_file_name('queries',Queries_Folder), % get absolute file name for './queries'
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prolog_file_name('output',Output_Folder), % get absolute file name for './output'
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problog_define_flag(bdd_directory, problog_flag_validate_directory, 'directory for BDD scripts', Queries_Folder,learning_general),
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problog_define_flag(output_directory, problog_flag_validate_directory, 'directory for logfiles etc', Output_Folder,learning_general,flags:learning_output_dir_handler),
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problog_define_flag(log_frequency, problog_flag_validate_posint, 'log results every nth iteration', 1, learning_general),
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problog_define_flag(rebuild_bdds, problog_flag_validate_nonegint, 'rebuild BDDs every nth iteration', 0, learning_general),
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problog_define_flag(learning_verbosity_level, problog_flag_validate_0to5,'How much output shall be given (0=nothing,5=all)',5, learning_general),
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problog_define_flag(reuse_initialized_bdds,problog_flag_validate_boolean, 'Reuse BDDs from previous runs',false, learning_general),
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problog_define_flag(check_duplicate_bdds,problog_flag_validate_boolean,'Store intermediate results in hash table',true,learning_general),
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problog_define_flag(init_method,problog_flag_validate_dummy,'ProbLog predicate to search proofs',(Query,Probability,BDDFile,ProbFile,problog_kbest_save(Query,100,Probability,_Status,BDDFile,ProbFile)),learning_general,flags:learning_init_handler),
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problog_define_flag(probability_initializer,problog_flag_validate_dummy,'Predicate to initialize probabilities',(FactID,P,random_probability(FactID,P)),learning_general,flags:learning_prob_init_handler),
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problog_define_flag(alpha,problog_flag_validate_number,'weight of negative examples (auto=n_p/n_n)',auto,learning_general,flags:auto_handler),
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problog_define_flag(sigmoid_slope,problog_flag_validate_posnumber,'slope of sigmoid function',1.0,learning_general),
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problog_define_flag(learning_rate,problog_flag_validate_posnumber,'Default learning rate (If line_search=false)',examples,learning_line_search,flags:examples_handler),
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problog_define_flag(line_search, problog_flag_validate_boolean,'estimate learning rate by line search',false,learning_line_search),
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problog_define_flag(line_search_never_stop, problog_flag_validate_boolean,'make tiny step if line search returns 0',true,learning_line_search),
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problog_define_flag(line_search_tau, problog_flag_validate_indomain_0_1_open,'tau value for line search',0.618033988749,learning_line_search),
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problog_define_flag(line_search_tolerance,problog_flag_validate_posnumber,'tolerance value for line search',0.05,learning_line_search),
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problog_define_flag(line_search_interval, problog_flag_validate_dummy,'interval for line search',(0,100),learning_line_search,flags:linesearch_interval_handler).
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init_logger :-
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logger_define_variable(iteration, int),
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logger_define_variable(duration,time),
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logger_define_variable(mse_trainingset,float),
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logger_define_variable(mse_min_trainingset,float),
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logger_define_variable(mse_max_trainingset,float),
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logger_define_variable(mse_testset,float),
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logger_define_variable(mse_min_testset,float),
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logger_define_variable(mse_max_testset,float),
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logger_define_variable(gradient_mean,float),
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logger_define_variable(gradient_min,float),
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logger_define_variable(gradient_max,float),
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logger_define_variable(ground_truth_diff,float),
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logger_define_variable(ground_truth_mindiff,float),
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logger_define_variable(ground_truth_maxdiff,float),
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logger_define_variable(learning_rate,float),
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logger_define_variable(alpha,float).
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%========================================================================
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%=
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%=
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%========================================================================
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:- initialization((init_flags,init_logger)).
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