1050 lines
35 KiB
Prolog
1050 lines
35 KiB
Prolog
%%% -*- Mode: Prolog; -*-
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%
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% $Date: 2011-04-21 14:18:59 +0200 (Thu, 21 Apr 2011) $
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% $Revision: 6364 $
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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, Vitor Santos Costa
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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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% keeping the Package available as open source and free software.
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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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reset_learning/0,
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sigmoid/3,
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inv_sigmoid/3
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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), [member/2,max_list/2, min_list/2, sum_list/2]).
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:- use_module(library(system), [file_exists/1, shell/2]).
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:- use_module(library(rbtrees)).
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:- use_module(library(lbfgs)).
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% load our own modules
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:- reexport(problog).
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:- use_module('problog/logger').
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:- use_module('problog/flags').
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:- use_module('problog/os').
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:- use_module('problog/print_learning').
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:- use_module('problog/utils_lbdd').
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:- use_module('problog/utils').
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:- use_module('problog/tabling').
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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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:- dynamic(query_is_similar/2).
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:- dynamic(query_md5/2).
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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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% 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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:- multifile(user:example/4).
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:- multifile(user:problog_discard_example/1).
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user:example(A,B,C,=) :-
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current_predicate(user:example/3),
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user:example(A,B,C),
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\+ user:problog_discard_example(B).
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:- multifile(user:test_example/4).
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user:test_example(A,B,C,=) :-
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current_predicate(user:test_example/3),
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user:test_example(A,B,C),
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\+ user:problog_discard_example(B).
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%========================================================================
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%= store the facts with the learned probabilities to a file
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%========================================================================
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save_model:-
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current_iteration(Iteration),
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create_factprobs_file_name(Iteration,Filename),
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export_facts(Filename).
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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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(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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(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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(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 probability 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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(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 probability 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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% 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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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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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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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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%=
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%========================================================================
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reset_learning :-
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retractall(learning_initialized),
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retractall(values_correct),
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retractall(current_iteration(_)),
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retractall(example_count(_)),
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% retractall(query_probability_intern(_,_)),%
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% retractall(query_gradient_intern(_,_,_,_)),
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retractall(last_mse(_)),
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retractall(query_is_similar(_,_)),
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retractall(query_md5(_,_,_)),
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set_problog_flag(alpha,auto),
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set_problog_flag(learning_rate,examples),
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logger_reset_all_variables.
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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
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%= 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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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).
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do_learning(_,_) :-
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format(user_error,'~n~Error: No training examples specified.~n~n',[]).
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do_learning_intern(0,_) :-
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!.
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do_learning_intern(Iterations,Epsilon) :-
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Iterations>0,
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init_learning,
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current_iteration(CurrentIteration),
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retractall(current_iteration(_)),
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NextIteration is CurrentIteration+1,
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assertz(current_iteration(NextIteration)),
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EndIteration is CurrentIteration+Iterations-1,
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format_learning(1,'~nIteration ~d of ~d~n',[CurrentIteration,EndIteration]),
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logger_set_variable(iteration,CurrentIteration),
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logger_start_timer(duration),
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% mse_testset,
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% ground_truth_difference,
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gradient_descent,
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problog_flag(log_frequency,Log_Frequency),
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(
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( Log_Frequency>0, 0 =:= CurrentIteration mod Log_Frequency)
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->
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once(save_model);
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true
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),
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update_values,
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|
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(
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last_mse(Last_MSE)
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->
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(
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retractall(last_mse(_)),
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logger_get_variable(mse_trainingset,Current_MSE),
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assertz(last_mse(Current_MSE)),
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!,
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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),
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assertz(last_mse(Current_MSE)),
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MSE_Diff is Epsilon+1
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)
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),
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(
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retractall(values_correct),
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retractall(query_is_similar(_,_)),
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retractall(query_md5(_,_,_)),
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empty_bdd_directory,
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init_queries
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),
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!,
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logger_stop_timer(duration),
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logger_write_data,
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RemainingIterations is Iterations-1,
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(
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MSE_Diff>Epsilon
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->
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do_learning_intern(RemainingIterations,Epsilon);
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true
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).
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|
|
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%========================================================================
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%= find proofs and build bdds for all training and test examples
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%=
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%=
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%========================================================================
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init_learning :-
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learning_initialized,
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!.
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init_learning :-
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check_examples,
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retractall(current_iteration(_)),
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assert(current_iteration(0)),
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% empty_output_directory,
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logger_write_header,
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format_learning(1,'Initializing everything~n',[]),
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succeeds_n_times(user:test_example(_,_,_,_),TestExampleCount),
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format_learning(3,'~q test examples~n',[TestExampleCount]),
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succeeds_n_times(user:example(_,_,_,_),TrainingExampleCount),
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assertz(example_count(TrainingExampleCount)),
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format_learning(3,'~q training examples~n',[TrainingExampleCount]),
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|
|
|
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% build BDD script for every example
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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once(init_queries),
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% done
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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assertz(current_iteration(-1)),
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assertz(learning_initialized),
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format_learning(1,'~n',[]).
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%========================================================================
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|
%= Updates all values of query_probability/2 and query_gradient/4
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|
%= should be called always before these predicates are accessed
|
|
%= if the old values are still valid, nothing happens
|
|
%========================================================================
|
|
|
|
update_values :-
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% delete old values
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
retractall(query_probability_intern(_,_)),
|
|
retractall(query_gradient_intern(_,_,_,_)).
|
|
|
|
|
|
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% Check, if continuous facts are used.
|
|
% if yes, switch to problog_exact
|
|
% continuous facts are not supported yet.
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
set_default_gradient_method :-
|
|
( problog_flag(continuous_facts, true )
|
|
->
|
|
problog_flag(init_method,(_,_,_,_,OldCall)),
|
|
(
|
|
(
|
|
continuous_fact(_),
|
|
OldCall\=problog_exact_save(_,_,_,_,_)
|
|
)
|
|
->
|
|
(
|
|
format_learning(2,'Theory uses continuous facts.~nWill use problog_exact/3 as initalization method.~2n',[]),
|
|
set_problog_flag(init_method,(Query,Probability,BDDFile,ProbFile,problog_exact_save(Query,Probability,_Status,BDDFile,ProbFile)))
|
|
);
|
|
true
|
|
)
|
|
;
|
|
problog_tabled(_)
|
|
->
|
|
(
|
|
format_learning(2,'Theory uses tabling.~nWill use problog_exact/3 as initalization method.~2n',[]),
|
|
set_problog_flag(init_method,(Query,Probability,BDDFile,ProbFile,problog_exact_save(Query,Probability,_Status,BDDFile,ProbFile)))
|
|
);
|
|
true
|
|
).
|
|
|
|
|
|
|
|
empty_bdd_directory :-
|
|
current_key(_,I),
|
|
integer(I),
|
|
recorded(I,bdd(_,_,_),R),
|
|
erase(R),
|
|
fail.
|
|
empty_bdd_directory.
|
|
|
|
|
|
|
|
%========================================================================
|
|
%= This predicate goes over all training and test examples,
|
|
%= calls the inference method of ProbLog and stores the resulting
|
|
%= BDDs
|
|
%========================================================================
|
|
|
|
|
|
init_queries :-
|
|
empty_bdd_directory,
|
|
format_learning(2,'Build BDDs for examples~n',[]),
|
|
forall(user:test_example(ID,Query,_Prob,_),init_one_query(ID,Query,test)),
|
|
forall(user:example(ID,Query,_Prob,_),init_one_query(ID,Query,training)).
|
|
|
|
bdd_input_file(Filename) :-
|
|
problog_flag(output_directory,Dir),
|
|
concat_path_with_filename(Dir,'input.txt',Filename).
|
|
|
|
init_one_query(QueryID,Query,_Type) :-
|
|
% format_learning(3,' ~q example ~q: ~q~n',[Type,QueryID,Query]),
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% if BDD file does not exist, call ProbLog
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
(
|
|
recorded(QueryID, _, _)
|
|
->
|
|
format_learning(3,' Reuse existing BDD ~q~n~n',[QueryID])
|
|
;
|
|
b_setval(problog_required_keep_ground_ids,false),
|
|
(QueryID mod 100 =:= 0 -> writeln(QueryID) ; true),
|
|
problog_flag(init_method,(Query,N,Bdd,graph2bdd(X,Y,N,Bdd))),
|
|
Query =.. [_,X,Y]
|
|
->
|
|
Bdd = bdd(Dir, Tree, MapList),
|
|
(
|
|
graph2bdd(X,Y,N,Bdd)
|
|
->
|
|
rb_new(H0),
|
|
maplist_to_hash(MapList, H0, Hash),
|
|
tree_to_grad(Tree, Hash, [], Grad)
|
|
% ;
|
|
% Bdd = bdd(-1,[],[]),
|
|
% Grad=[]
|
|
),
|
|
write('.'),
|
|
recordz(QueryID,bdd(Dir, Grad, MapList),_)
|
|
;
|
|
problog_flag(init_method,(Query,NOf,Bdd,problog_kbest_as_bdd(Call,NOf,Bdd))) ->
|
|
b_setval(problog_required_keep_ground_ids,false),
|
|
rb_new(H0),
|
|
strip_module(Call,_,Goal),
|
|
!,
|
|
Bdd = bdd(Dir, Tree, MapList),
|
|
% trace,
|
|
problog:problog_kbest_as_bdd(Goal,NOf,Bdd),
|
|
maplist_to_hash(MapList, H0, Hash),
|
|
Tree \= [],
|
|
%put_code(0'.),
|
|
tree_to_grad(Tree, Hash, [], Grad),
|
|
recordz(QueryID,bdd(Dir, Grad, MapList),_)
|
|
;
|
|
problog_flag(init_method,(Query,NOf,Bdd,Call)) ->
|
|
b_setval(problog_required_keep_ground_ids,false),
|
|
rb_new(H0),
|
|
Bdd = bdd(Dir, Tree, MapList),
|
|
% trace,
|
|
problog:Call,
|
|
maplist_to_hash(MapList, H0, Hash),
|
|
Tree \= [],
|
|
%put_code(0'.),
|
|
tree_to_grad(Tree, Hash, [], Grad),
|
|
recordz(QueryID,bdd(Dir, Grad, MapList),_)
|
|
).
|
|
|
|
|
|
|
|
|
|
%========================================================================
|
|
%=
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
query_probability(QueryID,Prob) :-
|
|
Prob <== qp[QueryID].
|
|
|
|
%========================================================================
|
|
%=
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
|
|
|
|
|
|
% FIXME
|
|
ground_truth_difference :-
|
|
findall(Diff,(tunable_fact(FactID,GroundTruth),
|
|
\+continuous_fact(FactID),
|
|
\+ var(GroundTruth),
|
|
%% get_fact_probability(FactID,Prob),
|
|
Prob <== p[FactID],
|
|
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 :-
|
|
current_iteration(Iteration),
|
|
create_training_predictions_file_name(Iteration,File_Name),
|
|
open(File_Name, write,Handle),
|
|
format_learning(2,'MSE_Training ',[]),
|
|
update_values,
|
|
findall(t(LogCurrentProb,SquaredError),
|
|
(user:training_example(QueryID,Query,TrueQueryProb,_Type),
|
|
once(update_query(QueryID,'+',probability)),
|
|
query_probability(QueryID,CurrentProb),
|
|
format(Handle,'ex(~q,training,~q,~q,~10f,~10f).~n',[Iteration,QueryID,Query,TrueQueryProb,CurrentProb]),
|
|
|
|
once(update_query_cleanup(QueryID)),
|
|
SquaredError is (CurrentProb-TrueQueryProb)**2,
|
|
LogCurrentProb is log(CurrentProb)
|
|
),
|
|
All),
|
|
maplist(tuple, All, AllLogs, AllSquaredErrors),
|
|
sum_list( AllLogs, LLH_Training_Queries),
|
|
close(Handle),
|
|
|
|
length(AllSquaredErrors,Length),
|
|
|
|
(
|
|
Length>0
|
|
->
|
|
(
|
|
sum_list(AllSquaredErrors,SumAllSquaredErrors),
|
|
min_list(AllSquaredErrors,MinError),
|
|
max_list(AllSquaredErrors,MaxError),
|
|
MSE is SumAllSquaredErrors/Length
|
|
);(
|
|
MSE=0.0,
|
|
MinError=0.0,
|
|
MaxError=0.0
|
|
)
|
|
),
|
|
|
|
logger_set_variable(mse_trainingset,MSE),
|
|
logger_set_variable(mse_min_trainingset,MinError),
|
|
logger_set_variable(mse_max_trainingset,MaxError),
|
|
logger_set_variable(llh_training_queries,LLH_Training_Queries),
|
|
format_learning(2,' (~8f)~n',[MSE]).
|
|
|
|
tuple(t(X,Y),X,Y).
|
|
|
|
mse_testset :-
|
|
current_iteration(Iteration),
|
|
create_test_predictions_file_name(Iteration,File_Name),
|
|
open(File_Name, write,Handle),
|
|
format_learning(2,'MSE_Test ',[]),
|
|
update_values,
|
|
bb_put(llh_test_queries,0.0),
|
|
findall(SquaredError,
|
|
(user:test_example(QueryID,Query,TrueQueryProb,Type),
|
|
once(update_query(QueryID,'+',probability)),
|
|
query_probability(QueryID,CurrentProb),
|
|
format(Handle,'ex(~q,test,~q,~q,~10f,~10f).~n',[Iteration,QueryID,Query,TrueQueryProb,CurrentProb]),
|
|
|
|
once(update_query_cleanup(QueryID)),
|
|
(
|
|
(Type == '='; (Type == '<', CurrentProb>QueryProb); (Type=='>',CurrentProb<QueryProb))
|
|
->
|
|
SquaredError is (CurrentProb-TrueQueryProb)**2;
|
|
SquaredError = 0.0
|
|
),
|
|
bb_get(llh_test_queries,Old_LLH_Test_Queries),
|
|
New_LLH_Test_Queries is Old_LLH_Test_Queries+log(CurrentProb),
|
|
bb_put(llh_test_queries,New_LLH_Test_Queries)
|
|
),
|
|
AllSquaredErrors),
|
|
|
|
close(Handle),
|
|
bb_delete(llh_test_queries,LLH_Test_Queries),
|
|
|
|
length(AllSquaredErrors,Length),
|
|
|
|
(
|
|
Length>0
|
|
->
|
|
(
|
|
sum_list(AllSquaredErrors,SumAllSquaredErrors),
|
|
min_list(AllSquaredErrors,MinError),
|
|
max_list(AllSquaredErrors,MaxError),
|
|
MSE is SumAllSquaredErrors/Length
|
|
);(
|
|
MSE=0.0,
|
|
MinError=0.0,
|
|
MaxError=0.0
|
|
)
|
|
),
|
|
|
|
logger_set_variable(mse_testset,MSE),
|
|
logger_set_variable(mse_min_testset,MinError),
|
|
logger_set_variable(mse_max_testset,MaxError),
|
|
logger_set_variable(llh_test_queries,LLH_Test_Queries),
|
|
format_learning(2,' (~8f)~n',[MSE]).
|
|
|
|
|
|
%========================================================================
|
|
%= Calculates the sigmoid function respectivly the inverse of it
|
|
%= warning: applying inv_sigmoid to 0.0 or 1.0 will yield +/-inf
|
|
%=
|
|
%= +Float, -Float
|
|
%========================================================================
|
|
|
|
sigmoid(T,Slope,Sig) :-
|
|
IN <== T,
|
|
OUT is 1/(1+exp(-IN*Slope)),
|
|
Sig <== OUT.
|
|
|
|
inv_sigmoid(T,Slope,InvSig) :-
|
|
InvSig <== -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 :-
|
|
old_prob <== p.
|
|
|
|
|
|
% vsc: avoid silly search
|
|
gradient_descent :-
|
|
problog_flag(sigmoid_slope,Slope),
|
|
% current_iteration(Iteration),
|
|
findall(FactID,tunable_fact(FactID,GroundTruth),L), length(L,N),
|
|
% leash(0),trace,
|
|
lbfgs_initialize(N,X,0,Solver),
|
|
forall(tunable_fact(FactID,GroundTruth),
|
|
(XZ is 0.0, X[FactID] <== XZ,sigmoid(XZ,Slope,Pr),set_fact_probability(FactID,Pr))),
|
|
problog_flag(sigmoid_slope,Slope),
|
|
lbfgs_run(Solver,_BestF),
|
|
lbfgs_finalize(Solver).
|
|
|
|
set_tunable(I,Slope,P) :-
|
|
X <== P[I],
|
|
sigmoid(X,Slope,Pr),
|
|
set_fact_probability(I,Pr).
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% start calculate gradient
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
user:evaluate(LLH_Training_Queries, X,Grad,N,_,_) :-
|
|
%Handle = user_error,
|
|
example_count(TrainingExampleCount),
|
|
LLs <== array[TrainingExampleCount ] of floats,
|
|
Probs <== array[N] of floats,
|
|
problog_flag(sigmoid_slope,Slope),
|
|
N1 is N-1,
|
|
forall(between(0,N1,I),
|
|
(Grad[I] <== 0.0, S <== X[I], sigmoid(S,Slope, P), Probs[I] <== P)
|
|
),
|
|
forall(
|
|
full_example(QueryID,QueryProb,BDD),
|
|
compute_grad(QueryID, BDD, QueryProb,Grad, Probs, Slope,LLs)
|
|
),
|
|
LLH_Training_QueriesSum <== sum(LLs),
|
|
LLH_Training_Queries is LLH_Training_QueriesSum/TrainingExampleCount .
|
|
%wrap(X, Grad, GradCount).
|
|
|
|
full_example(QueryID,QueryProb,BDD) :-
|
|
user:example(QueryID,_Query,QueryProb,_),
|
|
recorded(QueryID,BDD,_),
|
|
BDD = bdd(_Dir, _GradTree, MapList),
|
|
MapList = [_|_].
|
|
|
|
compute_grad(QueryID,BDD,QueryProb, Grad, Probs, Slope, LLs) :-
|
|
BDD = bdd(_Dir, _GradTree, MapList),
|
|
bind_maplist(MapList, Slope, Probs),
|
|
recorded(QueryID,BDD,_),
|
|
qprobability(BDD,Slope,BDDProb),
|
|
LL is (BDDProb-QueryProb)*(BDDProb-QueryProb),
|
|
LLs[QueryID] <== LL,
|
|
%writeln( qprobability(BDD,Slope,BDDProb) ),
|
|
forall(
|
|
member(I-_, MapList),
|
|
gradientpair(I, BDD,Slope,BDDProb, QueryProb, Grad, Probs)
|
|
).
|
|
|
|
gradientpair(I, BDD,Slope,BDDProb, QueryProb, Grad, Probs) :-
|
|
qgradient(I, BDD, Slope, FactID, GradValue),
|
|
% writeln(FactID),
|
|
G0 <== Grad[FactID],
|
|
Prob <== Probs[FactID],
|
|
%writeln( GN is G0-GradValue*(QueryProb-BDDProb)),
|
|
GN is G0-GradValue*2*Prob*(1-Prob)*(QueryProb-BDDProb),
|
|
%writeln(FactID:(G0->GN)),
|
|
Grad[FactID] <== GN.
|
|
|
|
qprobability(bdd(Dir, Tree, _MapList), Slope, Prob) :-
|
|
/* query_probability(21,6.775948e-01). */
|
|
run_sp(Tree, Slope, 1.0, Prob0),
|
|
(Dir == 1 -> Prob0 = Prob ; Prob is 1.0-Prob0).
|
|
|
|
|
|
qgradient(I, bdd(Dir, Tree, _MapList), Slope, I, Grad) :-
|
|
run_grad(Tree, I, Slope, 0.0, Grad0),
|
|
( Dir = 1 -> Grad = Grad0 ; Grad is -Grad0).
|
|
|
|
wrap( X, Grad, GradCount) :-
|
|
tunable_fact(FactID,GroundTruth),
|
|
Z<==X[FactID],
|
|
W<==Grad[FactID],
|
|
WC<==GradCount[FactID],
|
|
WC > 0,
|
|
format('ex(~d, ~q, ~4f, ~4f).~n',[FactID,GroundTruth,Z,W]),
|
|
% Grad[FactID] <== WN,
|
|
fail.
|
|
wrap( _X, _Grad, _GradCount).
|
|
|
|
|
|
% writeln(grad(QueryID:I:Grad)),
|
|
% assert(query_gradient_intern(QueryID,I,p,Grad)),
|
|
% fail.
|
|
%gradient(QueryID, g, Slope) :-
|
|
% gradient(QueryID, l, Slope).
|
|
|
|
maplist_to_hash([], H0, H0).
|
|
maplist_to_hash([I-V|MapList], H0, Hash) :-
|
|
rb_insert(H0, V, I, H1),
|
|
maplist_to_hash(MapList, H1, Hash).
|
|
|
|
tree_to_grad([], _, Grad, Grad).
|
|
tree_to_grad([Node|Tree], H, Grad0, Grad) :-
|
|
node_to_gradient_node(Node, H, GNode),
|
|
tree_to_grad(Tree, H, [GNode|Grad0], Grad).
|
|
|
|
node_to_gradient_node(pp(P-G,X,L,R), H, gnodep(P,G,X,Id,PL,GL,PR,GR)) :-
|
|
rb_lookup(X,Id,H),
|
|
(L == 1 -> GL=0, PL=1 ; L == 0 -> GL = 0, PL=0 ; L = PL-GL),
|
|
(R == 1 -> GR=0, PR=1 ; R == 0 -> GR = 0, PR=0 ; R = PR-GR).
|
|
node_to_gradient_node(pn(P-G,X,L,R), H, gnoden(P,G,X,Id,PL,GL,PR,GR)) :-
|
|
rb_lookup(X,Id,H),
|
|
(L == 1 -> GL=0, PL=1 ; L == 0 -> GL = 0, PL=0 ; L = PL-GL),
|
|
(R == 1 -> GR=0, PR=1 ; R == 0 -> GR = 0, PR=0 ; R = PR-GR).
|
|
|
|
run_sp([], _, P0, P0).
|
|
run_sp(gnodep(P,_G, EP, _Id, PL, _GL, PR, _GR).Tree, Slope, _, PF) :-
|
|
P is EP*PL+ (1.0-EP)*PR,
|
|
run_sp(Tree, Slope, P, PF).
|
|
run_sp(gnoden(P,_G, EP, _Id, PL, _GL, PR, _GR).Tree, Slope, _, PF) :-
|
|
P is EP*PL + (1.0-EP)*(1.0 - PR),
|
|
run_sp(Tree, Slope, P, PF).
|
|
|
|
run_grad([], _I, _, G0, G0).
|
|
run_grad([gnodep(P,G, EP, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :-
|
|
P is EP*PL+ (1.0-EP)*PR,
|
|
G0 is EP*GL + (1.0-EP)*GR,
|
|
% don' t forget the -X
|
|
( I == Id -> G is PL-PR ; G = G0 ),
|
|
run_grad(Tree, I, Slope, G, GF).
|
|
run_grad([gnoden(P,G, EP, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :-
|
|
P is EP*PL + (1.0-EP)*(1.0 - PR),
|
|
G0 is EP*GL - (1.0 - EP) * GR,
|
|
( I == Id -> G is PL-(1.0-PR) ; G = G0 ),
|
|
run_grad(Tree, I, Slope, G, GF).
|
|
|
|
|
|
|
|
prob2log(_X,Slope,FactID,V) :-
|
|
get_fact_probability(FactID, V0),
|
|
inv_sigmoid(V0, Slope, V).
|
|
|
|
log2prob(X,Slope,FactID,V) :-
|
|
V0 <== X[FactID],
|
|
sigmoid(V0, Slope, V).
|
|
|
|
bind_maplist([], _Slope, _X).
|
|
bind_maplist([Node-Pr|MapList], Slope, X) :-
|
|
Pr <== X[Node],
|
|
bind_maplist(MapList, Slope, X).
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% stop calculate gradient
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
user:progress(FX,X,_G,X_Norm,G_Norm,Step,_N,Iteration,Ls,0) :-
|
|
problog_flag(sigmoid_slope,Slope),
|
|
forall(tunable_fact(FactID,_GroundTruth), set_tunable(FactID,Slope,X)),
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current_iteration(CurrentIteration),
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retractall(current_iteration(_)),
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NextIteration is CurrentIteration+1,
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assertz(current_iteration(NextIteration)),
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save_model,
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logger_set_variable(mse_trainingset, FX),
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X0 <== X[0], sigmoid(X0,Slope,P0),
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X1 <== X[1], sigmoid(X1,Slope,P1),
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format('~d. Iteration : (x0,x1)=(~4f,~4f) f(X)=~4f |X|=~4f |X\'|=~4f Step=~4f Ls=~4f~n',[Iteration,P0 ,P1,FX,X_Norm,G_Norm,Step,Ls]).
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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_problog_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(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,Tree,problog:problog_kbest_as_bdd(Query,100,Tree)),learning_general,flags:learning_libdd_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(continuous_facts,problog_flag_validate_boolean,'support parameter learning of continuous distributions',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),
|
|
logger_define_variable(mse_max_testset,float),
|
|
logger_define_variable(gradient_mean,float),
|
|
logger_define_variable(gradient_min,float),
|
|
logger_define_variable(gradient_max,float),
|
|
logger_define_variable(ground_truth_diff,float),
|
|
logger_define_variable(ground_truth_mindiff,float),
|
|
logger_define_variable(ground_truth_maxdiff,float),
|
|
logger_define_variable(learning_rate,float),
|
|
logger_define_variable(alpha,float),
|
|
logger_define_variable(llh_training_queries,float),
|
|
logger_define_variable(llh_test_queries,float).
|
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|
|
:- initialization(init_flags).
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|
|
:- initialization(init_logger).
|