1182 lines
38 KiB
Prolog
1182 lines
38 KiB
Prolog
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%%% -*- Mode: Prolog; -*-
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%
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% $Date: 2011-12-05 14:07:19 +0100 (Mon, 05 Dec 2011) $
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% $Revision: 6766 $
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%
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% This file is part of ProbLog
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% http://dtai.cs.kuleuven.be/problog
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%
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% ProbLog was developed at Katholieke Universiteit Leuven
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%
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% Copyright 2009
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% Angelika Kimmig, Vitor Santos Costa, Bernd Gutmann
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%
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% Main author 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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% redistributed. The intent is that the Copyright Holder maintains some
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% artistic control over the development of that Package while still
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% keeping the Package available as open source and free software.
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% You are always permitted to make arrangements wholly outside of this
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% license directly with the Copyright Holder of a given Package. If the
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% (13) This license includes the non-exclusive, worldwide,
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%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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:-source.
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:- module(problog_lfi,[do_learning/1,
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do_learning/2,
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create_ground_tunable_fact/2,
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reset_learning/0
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]).
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% switch on all the checks to reduce bug searching time
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:- style_check(all).
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:- yap_flag(unknown,error).
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% load modules from the YAP library
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:- use_module(library(lists),[member/2,nth1/3,sum_list/2,min_list/2,max_list/2]).
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:- use_module(library(system),[file_exists/1,exec/3,wait/2]).
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% load our own modules
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:- use_module('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/completion').
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:- use_module('problog/print_learning').
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:- use_module('problog/utils_learning').
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:- use_module('problog/utils').
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:- use_module('problog/ad_converter').
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% used to indicate the state of the system
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:- dynamic(learning_initialized/0).
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:- dynamic(current_iteration/1).
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:- dynamic(query_all_scripts/2).
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:- dynamic(last_llh/1).
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:- discontiguous(user:myclause/1).
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:- discontiguous(user:myclause/2).
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:- discontiguous(user:known/3).
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:- discontiguous(user:example/1).
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:- discontiguous(user:test_example/1).
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:- multifile(completion:bdd_cluster/2).
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%:- multifile(completion:known_count/4).
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user:term_expansion(myclause((Head<--Body)), C) :-
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prolog_load_context(module,Module),
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term_expansion_intern_ad((Head<--Body), Module,lfi_learning, C).
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%========================================================================
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%= Hack for Ingo, to allow tunable facts with body
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%=
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%= e.g. :- create_ground_tunable_fact( t(_) :: f(X), member(X,[a,b,c])).
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%= will create
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%= t(_) :: f(a).
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%= t(_) :: f(b).
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%= t(_) :: f(c).
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%========================================================================
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create_ground_tunable_fact(F,B) :-
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B,
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once(problog_assert(F)),
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fail.
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create_ground_tunable_fact(_,_).
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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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create_factprobs_file_name(Iteration,Filename),
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open(Filename,'write',Handle),
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forall((current_predicate(user:ad_intern/3),user:ad_intern(Original,ID,Facts)),
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print_ad_intern(Handle,Original,ID,Facts)
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),
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forall(probabilistic_fact(_,Goal,ID),
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(
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array_element(factprob,ID,P),
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(
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is_mvs_aux_fact(Goal)
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->
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format(Handle,'% ~10f :: ~q. %ID=~q~n',[P,Goal,ID]);
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format(Handle ,'~10f :: ~q. %ID=~q~n',[P,Goal,ID])
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)
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)
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),
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close(Handle).
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is_mvs_aux_fact(A) :-
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functor(A,B,_),
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atomic_concat(mvs_fact_,_,B).
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print_ad_intern(Handle,(Head<--Body),_ID,Facts) :-
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format(Handle,'myclause( (',[]),
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print_ad_intern(Head,Facts,0.0,Handle),
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format(Handle,' <-- ~q) ).~n',[Body]).
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print_ad_intern((A1;B1),[A2|B2],Mass,Handle) :-
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once(print_ad_intern_one(A1,A2,Mass,NewMass,Handle)),
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format(Handle,'; ',[]),
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print_ad_intern(B1,B2,NewMass,Handle).
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print_ad_intern(_::Fact,[],Mass,Handle) :-
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P2 is 1.0 - Mass,
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format(Handle,'~f :: ~q',[P2,Fact]).
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print_ad_intern_one(_::Fact,_::AuxFact,Mass,NewMass,Handle) :-
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% ask problog to get the fact_id
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once(probabilistic_fact(_,AuxFact,FactID)),
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% look in our table for the probability
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array_element(factprob,FactID,P),
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P2 is P * (1-Mass),
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NewMass is Mass+P2,
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format(Handle,'~f :: ~q',[P2,Fact]).
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%========================================================================
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%= initialize everything and perform Iterations times EM
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%= can be called several times
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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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integer(Iterations),
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number(Epsilon),
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Iterations>0,
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init_learning,
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!,
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do_learning_intern(Iterations,Epsilon),
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!,
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copy_back_fact_probabilities.
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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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logger_start_timer(duration),
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current_iteration(CurrentIteration),
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!,
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retractall(current_iteration(_)),
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!,
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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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write_probabilities_file,
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once(llh_testset),
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once(ground_truth_difference),
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once(em_one_iteration),
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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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!,
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(
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last_llh(Last_LLH)
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->
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(
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retractall(last_llh(_)),
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logger_get_variable(llh_training_set,Current_LLH),
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assertz(last_llh(Current_LLH)),
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!,
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LLH_Diff is abs(Last_LLH-Current_LLH)
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); (
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logger_get_variable(llh_training_set,Current_LLH),
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assertz(last_llh(Current_LLH)),
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LLH_Diff is Epsilon+1
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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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garbage_collect,
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!,
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(
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LLH_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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convert_filename_to_problog_path('simplecudd_lfi', Path),
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(
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file_exists(Path)
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->
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true;
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(
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problog_path(PD),
|
|
format(user_error, 'WARNING: Can not find file: simplecudd_lfi. Please place file in problog path: ~q~n',[PD]),
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|
fail
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|
)
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|
),
|
|
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|
check_theory,
|
|
|
|
|
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
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% Delete the stuff from the previous run
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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problog_flag(reuse_initialized_bdds,Re_Use_Flag),
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|
|
|
(
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Re_Use_Flag==false
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->
|
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empty_bdd_directory;
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true
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|
),
|
|
empty_output_directory,
|
|
|
|
|
|
logger_write_header,
|
|
|
|
format_learning(1,'Initializing everything~n',[]),
|
|
|
|
(
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|
current_predicate(user:test_example/1)
|
|
->
|
|
(
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succeeds_n_times(user:test_example(_),TestExampleCount),
|
|
format_learning(3,'~q test example(s)~n',[TestExampleCount])
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);
|
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true
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|
),
|
|
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|
succeeds_n_times(user:example(_),TrainingExampleCount),
|
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format_learning(3,'~q training example(s)~n',[TrainingExampleCount]),
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% Create arrays for probabilities and counting tables
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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|
once(initialize_fact_probabilities),
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|
problog:probclause_id(N),
|
|
static_array(factprob_temp,N,float),
|
|
static_array(factusage,N,int),
|
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static_array(known_count_true_training,N,int),
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static_array(known_count_false_training,N,int),
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static_array(known_count_true_test,N,int),
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static_array(known_count_false_test,N,int),
|
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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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|
% done
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
assertz(current_iteration(0)),
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|
assertz(learning_initialized),
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once(save_model),
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|
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format_learning(1,'~n',[]),
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garbage_collect,
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garbage_collect_atoms.
|
|
|
|
|
|
|
|
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%========================================================================
|
|
%= This predicate checks some aspects of the data given by the user.
|
|
%= You know folks: Garbage in, garbage out.
|
|
%=
|
|
%========================================================================
|
|
check_theory :-
|
|
(
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|
(user:myclause(Head,Body),P :: Head)
|
|
->
|
|
(
|
|
format(user_error,'===============================================================~n',[]),
|
|
format(user_error,' The theory contains an atom that appears both as probabilistic~n',[]),
|
|
format(user_error,' fact and as head of an rule. This is not allowed.~2n',[]),
|
|
format(user_error,' ~q~n',[P :: Head]),
|
|
format(user_error,' ~q~2n',[myclause(Head,Body)]),
|
|
format(user_error,'===============================================================~2n',[]),
|
|
|
|
throw(bad_theory(Head))
|
|
);
|
|
true
|
|
),
|
|
|
|
(
|
|
(current_predicate(user:example/1),user:example(_))
|
|
->
|
|
true;
|
|
(
|
|
format(user_error,'===============================================================~n',[]),
|
|
format(user_error,' No training examples specified.~n',[]),
|
|
format(user_error,'===============================================================~2n',[]),
|
|
throw(bad_theory(no_training_examples))
|
|
)
|
|
),
|
|
|
|
(
|
|
( current_predicate(user:test_example/1),user:example(ID), user:test_example(ID) )
|
|
->
|
|
(
|
|
format(user_error,'===============================================================~n',[]),
|
|
format(user_error,' The example ~q appears both as test and as training example.~n',[ID]),
|
|
format(user_error,' Example IDs from test and training examples must be disjoint.~2n',[]),
|
|
format(user_error,' Do NOT bypass this test, since the implementation yields wrong resuls~n',[]),
|
|
format(user_error,' when an example ID appears both as test and training example.',[]),
|
|
format(user_error,'===============================================================~2n',[]),
|
|
|
|
throw(bad_theory(double_id(ID)))
|
|
);
|
|
true
|
|
),
|
|
|
|
(
|
|
(current_predicate(user:known/3),user:example(ID2),user:known(ID2,_,_))
|
|
->
|
|
true;
|
|
(
|
|
format(user_error,'===============================================================~n',[]),
|
|
format(user_error,' No evidence specified.~n',[]),
|
|
format(user_error,'===============================================================~2n',[]),
|
|
throw(bad_theory(no_evidence))
|
|
)
|
|
),
|
|
|
|
|
|
(
|
|
(user:known(ID,Foo,Evidence), (Evidence\=true,Evidence\=false))
|
|
->
|
|
(
|
|
format(user_error,'===============================================================~n',[]),
|
|
format(user_error,' Bad evidence for training example ~q: ~q.~n',[ID,known(ID,Foo,Evidence)]),
|
|
format(user_error,'===============================================================~2n',[]),
|
|
throw(bad_theory(bad_evidence(ID)))
|
|
);
|
|
true
|
|
),
|
|
|
|
(
|
|
(user:known(ID,Foo,true), user:known(ID,Foo,false))
|
|
->
|
|
(
|
|
format(user_error,'===============================================================~n',[]),
|
|
format(user_error,' Bad evidence for training example ~q: ~q and ~q~n',[ID,known(ID,Foo,true),known(ID,Foo,false)]),
|
|
format(user_error,'===============================================================~2n',[]),
|
|
throw(bad_theory(bad_evidence(ID)))
|
|
);
|
|
true
|
|
).
|
|
|
|
|
|
|
|
%========================================================================
|
|
%= copy fact probabilities to array for speeding up the update
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
|
|
initialize_fact_probabilities :-
|
|
problog:probclause_id(N),
|
|
static_array(factprob,N,float),
|
|
|
|
forall(get_fact_probability(FactID,P),
|
|
update_array(factprob,FactID,P)).
|
|
|
|
copy_back_fact_probabilities :-
|
|
forall(tunable_fact(FactID,_),
|
|
(
|
|
array_element(factprob,FactID,P),
|
|
set_fact_probability(FactID,P)
|
|
)
|
|
).
|
|
|
|
|
|
|
|
%========================================================================
|
|
%= This predicate goes over all training and test examples,
|
|
%= calls the inference method of ProbLog and stores the resulting
|
|
%= BDDs
|
|
%========================================================================
|
|
|
|
|
|
init_queries :-
|
|
problog_flag(cluster_bdds,Cluster_BDDs),
|
|
format_learning(2,'Build BDDs for examples~n',[]),
|
|
forall(user:example(Training_ID),
|
|
(
|
|
format_learning(3,'training example ~q: ',[Training_ID]),
|
|
init_one_query(Training_ID,training)
|
|
)
|
|
),
|
|
|
|
forall(
|
|
(
|
|
current_predicate(user:test_example/1),
|
|
user:test_example(Test_ID)
|
|
),
|
|
(
|
|
format_learning(3,'test example ~q: ',[Test_ID]),
|
|
init_one_query(Test_ID,test)
|
|
)
|
|
),
|
|
|
|
(
|
|
Cluster_BDDs==true
|
|
->
|
|
(
|
|
format_learning(2,'Calculate MD5s for training example BDD scripts~n',[]),
|
|
create_training_query_cluster_list(Training_Set_Cluster_List),
|
|
format_learning(2,'Calculate MD5s for test example BDD scripts~n',[]),
|
|
create_test_query_cluster_list(Test_Set_Cluster_List)
|
|
);
|
|
(
|
|
findall( a(QueryID,ClusterID,1), (
|
|
current_predicate(user:test_example/1),
|
|
user:test_example(QueryID),
|
|
bdd_cluster(QueryID,ClusterIDs),
|
|
member(ClusterID,ClusterIDs)
|
|
), Test_Set_Cluster_List),
|
|
|
|
findall( a(QueryID,ClusterID,1), (
|
|
user:example(QueryID),
|
|
bdd_cluster(QueryID,ClusterIDs),
|
|
member(ClusterID,ClusterIDs)
|
|
), Training_Set_Cluster_List)
|
|
)
|
|
),
|
|
|
|
assertz(training_set_cluster_list(Training_Set_Cluster_List)),
|
|
assertz(test_set_cluster_list(Test_Set_Cluster_List)).
|
|
|
|
%========================================================================
|
|
%=
|
|
%========================================================================
|
|
|
|
init_one_query(QueryID,_Query_Type) :-
|
|
create_known_values_file_name(QueryID,File_Name),
|
|
file_exists(File_Name),
|
|
!,
|
|
format_learning(3,'Will reuse existing BDD script ~q for example ~q.~n',[File_Name,QueryID]),
|
|
consult(File_Name).
|
|
|
|
%FIXME
|
|
|
|
% check whether we can read the BDD script for each cluster
|
|
|
|
init_one_query(QueryID,Query_Type) :-
|
|
once(propagate_evidence(QueryID,Query_Type)),
|
|
format_learning(3,'~n',[]),
|
|
garbage_collect_atoms,
|
|
garbage_collect.
|
|
|
|
|
|
create_test_query_cluster_list(L2) :-
|
|
findall( a(QueryID,ClusterID), (
|
|
current_predicate(user:test_example/1),
|
|
user:test_example(QueryID),
|
|
bdd_cluster(QueryID,ClusterIDs),
|
|
member(ClusterID,ClusterIDs)
|
|
), AllCluster),
|
|
calc_all_md5(AllCluster,AllCluster2),
|
|
findall(a(QueryID1,ClusterID1,Len),(bagof(a(QueryID,ClusterID),member(a(QueryID,ClusterID,_MD5),AllCluster2),L),nth1(1,L,a(QueryID1,ClusterID1)),length(L,Len)),L2),
|
|
!,
|
|
length(AllCluster,Len1),
|
|
length(L2,Len2),
|
|
(
|
|
Len1>0
|
|
->
|
|
(
|
|
Reduction is Len2/Len1,
|
|
format_learning(3,' ~d cluster after splitting, ~d unique cluster ==> reduction factor of ~4f~n',[Len1,Len2,Reduction])
|
|
);
|
|
true
|
|
).
|
|
|
|
calc_all_md5([],[]).
|
|
calc_all_md5([a(QueryID,ClusterID)|T],[a(QueryID,ClusterID,MD5)|T2]) :-
|
|
create_bdd_file_name(QueryID,ClusterID,File_Name),
|
|
calc_md5(File_Name,MD5),
|
|
calc_all_md5(T,T2).
|
|
|
|
create_training_query_cluster_list(L2) :-
|
|
findall( a(QueryID,ClusterID), (
|
|
user:example(QueryID),
|
|
bdd_cluster(QueryID,ClusterIDs),
|
|
member(ClusterID,ClusterIDs)
|
|
), AllCluster),
|
|
|
|
calc_all_md5(AllCluster,AllCluster2),
|
|
findall(a(QueryID1,ClusterID1,Len),
|
|
(
|
|
bagof(a(QueryID,ClusterID),member(a(QueryID,ClusterID,_MD5),AllCluster2),L),
|
|
nth1(1,L,a(QueryID1,ClusterID1)),
|
|
length(L,Len)
|
|
),L2),
|
|
length(AllCluster,Len1),
|
|
length(L2,Len2),
|
|
|
|
Reduction is Len2/Len1,
|
|
|
|
format_learning(3,' ~d cluster after splitting, ~d unique cluster ==> reduction factor of ~4f~n',[Len1,Len2,Reduction]).
|
|
|
|
|
|
%========================================================================
|
|
%=
|
|
%========================================================================
|
|
|
|
reset_learning :-
|
|
(
|
|
learning_initialized
|
|
->
|
|
(
|
|
retractall(current_iteration(_)),
|
|
retractall(learning_initialized),
|
|
|
|
retractall(training_set_cluster_list(_)),
|
|
retractall(test_set_cluster_list(_)),
|
|
close_static_array(factprob),
|
|
close_static_array(factprob_temp),
|
|
close_static_array(factusage),
|
|
|
|
close_static_array(known_count_true_training),
|
|
close_static_array(known_count_false_training),
|
|
close_static_array(known_count_true_test),
|
|
close_static_array(known_count_false_test),
|
|
|
|
reset_completion,
|
|
empty_bdd_directory,
|
|
empty_output_directory,
|
|
|
|
logger_reset_all_variables
|
|
);
|
|
true
|
|
).
|
|
|
|
%========================================================================
|
|
%= calculate the LLH on the test set and set the variable
|
|
%= in the logger module
|
|
%========================================================================
|
|
|
|
llh_testset :-
|
|
current_predicate(user:test_example/1),
|
|
!,
|
|
current_iteration(Iteration),
|
|
create_test_predictions_file_name(Iteration,F),
|
|
|
|
open(F,'write',Handle),
|
|
|
|
catch(
|
|
sum_forall(LProb,
|
|
(
|
|
probabilistic_fact(_,_,FactID),
|
|
array_element(factprob,FactID,PFact),
|
|
array_element(known_count_true_test,FactID,KK_True),
|
|
array_element(known_count_false_test,FactID,KK_False),
|
|
|
|
(
|
|
KK_True>0
|
|
->
|
|
Part1 is KK_True*log(PFact);
|
|
Part1 is 0.0
|
|
),
|
|
(
|
|
KK_False>0
|
|
->
|
|
LProb is Part1+KK_False*log(1-PFact);
|
|
LProb is Part1
|
|
)
|
|
),
|
|
PropagatedLLH
|
|
),_,PropagatedLLH is 0.0/0.0),
|
|
format(Handle,'prob_known_atoms(~15e).~n',[PropagatedLLH]),
|
|
|
|
test_set_cluster_list(AllCluster),
|
|
% deal with test examples where BDD needs to be evaluated
|
|
problog_flag(parallel_processes,Parallel_Processes),
|
|
once(evaluate_bdds(AllCluster,Handle,Parallel_Processes,'d',':',PropagatedLLH,LLH)),
|
|
logger_set_variable(llh_test_set,LLH),
|
|
close(Handle).
|
|
llh_testset :-
|
|
true.
|
|
|
|
|
|
|
|
|
|
|
|
%========================================================================
|
|
%=
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
|
|
% FIXME
|
|
ground_truth_difference :-
|
|
findall(Diff,(tunable_fact(FactID,GroundTruth),
|
|
\+continuous_fact(FactID),
|
|
\+ var(GroundTruth),
|
|
array_element(factprob,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).
|
|
|
|
%========================================================================
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
|
|
write_probabilities_file :-
|
|
current_iteration(Iteration),
|
|
create_bdd_input_file_name(Iteration,Probabilities_File),
|
|
open(Probabilities_File,'write',Handle),
|
|
forall(get_fact_probability(ID,_),
|
|
(
|
|
array_element(factprob,ID,Prob),
|
|
|
|
(
|
|
non_ground_fact(ID)
|
|
->
|
|
format(Handle,'@x~q_*~n~15e~n1~nx~q~N',[ID,Prob,ID]);
|
|
format(Handle,'@x~q~n~15e~n1~nx~q~N',[ID,Prob,ID])
|
|
)
|
|
)
|
|
),
|
|
close(Handle).
|
|
|
|
|
|
|
|
|
|
%========================================================================
|
|
%=
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
|
|
update_query(QueryID,ClusterID ,Method,Command,PID,Output_File_Name) :-
|
|
current_iteration(Iteration),
|
|
|
|
create_bdd_input_file_name(Iteration,Input_File_Name),
|
|
create_bdd_output_file_name(QueryID,ClusterID,Iteration,Output_File_Name),
|
|
create_bdd_file_name(QueryID,ClusterID,BDD_File_Name),
|
|
|
|
convert_filename_to_problog_path('simplecudd_lfi',Absolute_Name),
|
|
|
|
atomic_concat([Absolute_Name,
|
|
' -i "', Input_File_Name, '"',
|
|
' -l "', BDD_File_Name, '"',
|
|
' -m ', Method,
|
|
' -id ', QueryID],Command),
|
|
open( Output_File_Name, write, Stream ),
|
|
exec(Command,[std, Stream ,std],PID),
|
|
close( Stream ).
|
|
|
|
update_query_wait(QueryID,_ClusterID,Count,Symbol,Command,PID,OutputFilename,BDD_Probability) :-
|
|
wait(PID,Error),
|
|
format_learning(4,'~w',[Symbol]),
|
|
(
|
|
Error \= 0
|
|
->
|
|
(
|
|
format(user_error,'SimpleCUDD stopped with error code ~q.~n', [Error]),
|
|
format(user_error,'The command was~n ~q~n',[Command]),
|
|
throw(bdd_error(QueryID,Error))
|
|
);
|
|
true
|
|
),
|
|
|
|
once(my_load_allinone(OutputFilename,QueryID,Count,BDD_Probability)),
|
|
|
|
problog_flag(retain_bdd_output,Retain_BDD_Output),
|
|
|
|
(
|
|
Retain_BDD_Output==true
|
|
->
|
|
true;
|
|
delete_file_silently(OutputFilename)
|
|
).
|
|
|
|
|
|
%========================================================================
|
|
%=
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
|
|
|
|
my_load_allinone(File,QueryID,Count,BDD_Probability) :-
|
|
open(File,'read',Handle),
|
|
read(Handle,Atom),
|
|
once(my_load_intern_allinone(Atom,Handle,QueryID,Count,error,BDD_Probability)),
|
|
!,
|
|
close(Handle).
|
|
|
|
my_load_allinone(File,QueryID,_,_,_,_) :-
|
|
format(user_error,'Error at ~q.~2n',[my_load(File,QueryID)]),
|
|
throw(error(my_load(File,QueryID))).
|
|
|
|
my_load_intern_allinone(end_of_file,_,_,_,BDD_Probability,BDD_Probability) :-
|
|
!.
|
|
my_load_intern_allinone(query_probability(QueryID,Prob),Handle,QueryID,Count,Old_BDD_Probability,BDD_Probability) :-
|
|
!,
|
|
(
|
|
Old_BDD_Probability==error
|
|
->
|
|
true;
|
|
throw(error(bdd_output_contains_prob_twice(query_probability(QueryID,Prob))))
|
|
),
|
|
Prob2 is Prob*Count, % this is will throw an exception if simplecudd delivers non-number garbage
|
|
read(Handle,X),
|
|
my_load_intern_allinone(X,Handle,QueryID,Count,Prob2,BDD_Probability).
|
|
my_load_intern_allinone(ec(QueryID,VarName,Value),Handle,QueryID,Count,Old_BDD_Probability,BDD_Probability) :-
|
|
!,
|
|
split_atom_name(VarName,FactID,_GroundID),
|
|
MultValue is Value*Count,
|
|
add_to_array_element(factprob_temp,FactID,MultValue,_NewEC),
|
|
add_to_array_element(factusage,FactID,Count,_NewDiv),
|
|
read(Handle,X),
|
|
my_load_intern_allinone(X,Handle,QueryID,Count,Old_BDD_Probability,BDD_Probability).
|
|
my_load_intern_allinone(X,Handle,QueryID,Count,Old_BDD_Probability,BDD_Probability) :-
|
|
format(user_error,'Unknown atom ~q in results file.~n',[X]),
|
|
read(Handle,X2),
|
|
my_load_intern_allinone(X2,Handle,QueryID,Count,Old_BDD_Probability,BDD_Probability).
|
|
|
|
%========================================================================
|
|
%= Perform one iteration of EM
|
|
%========================================================================
|
|
|
|
my_reset_static_array(Name) :-
|
|
%%% DELETE ME AFTER VITOR FIXED HIS BUG
|
|
static_array_properties(Name,Size,Type),
|
|
LastPos is Size-1,
|
|
(
|
|
Type==int
|
|
->
|
|
forall(between(0,LastPos,Pos), update_array(Name,Pos,0))
|
|
;
|
|
Type==float
|
|
->
|
|
forall(between(0,LastPos,Pos), update_array(Name,Pos,0.0))
|
|
;
|
|
fail
|
|
).
|
|
|
|
em_one_iteration :-
|
|
write_probabilities_file,
|
|
my_reset_static_array(factprob_temp),
|
|
my_reset_static_array(factusage),
|
|
|
|
current_iteration(Iteration),
|
|
create_training_predictions_file_name(Iteration,Name),
|
|
|
|
open(Name,'write',Handle),
|
|
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% start calculate new values
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
|
|
% process known_count information
|
|
bb_put(dummy,0.0),
|
|
(
|
|
% go over all tunable facts and get their current probability
|
|
tunable_fact(FactID,_),
|
|
array_element(factprob,FactID,P),
|
|
% get known counts
|
|
|
|
array_element(known_count_true_training,FactID,KK_True),
|
|
array_element(known_count_false_training,FactID,KK_False),
|
|
KK_Sum is KK_True+KK_False,
|
|
|
|
KK_Sum>0,
|
|
|
|
% add counts
|
|
add_to_array_element(factprob_temp,FactID,KK_True,_NewValue),
|
|
add_to_array_element(factusage,FactID,KK_Sum,_NewCount),
|
|
|
|
% for LLH training set
|
|
|
|
(
|
|
KK_True>0
|
|
->
|
|
Part1 is KK_True*log(P);
|
|
Part1 is 0.0
|
|
),
|
|
(
|
|
KK_False>0
|
|
->
|
|
LProb is Part1 + KK_False*log(1-P);
|
|
LProb is Part1
|
|
),
|
|
|
|
bb_get(dummy,Old),
|
|
New is Old+LProb,
|
|
bb_put(dummy,New),
|
|
|
|
fail;
|
|
true
|
|
),
|
|
bb_delete(dummy,LLH_From_True_BDDs),
|
|
|
|
format(Handle,'propagatedprob(~15e).~n',[LLH_From_True_BDDs]),
|
|
|
|
training_set_cluster_list(AllCluster),
|
|
|
|
problog_flag(parallel_processes,Parallel_Processes),
|
|
evaluate_bdds(AllCluster,Handle,Parallel_Processes,'e','.',LLH_From_True_BDDs,LLH),
|
|
|
|
logger_set_variable(llh_training_set,LLH),
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% stop calculate new values
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
format_learning(2,'~n',[]),
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% start copy new values
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
|
|
problog_flag(pc_numerator,Pseudo_Counts_Numerator),
|
|
problog_flag(pc_denominator,Pseudo_Counts_Denominator),
|
|
|
|
forall(
|
|
(
|
|
tunable_fact(FactID,_),
|
|
array_element(factusage,FactID,Used),
|
|
Used>0 % only update relevant facts
|
|
),
|
|
(
|
|
array_element(factprob_temp,FactID,NewValue),
|
|
NewP is (NewValue+ Pseudo_Counts_Numerator) / (Used+Pseudo_Counts_Denominator),
|
|
update_array(factprob,FactID,NewP)
|
|
)
|
|
),
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% stop copy new values
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
|
|
close(Handle).
|
|
|
|
|
|
%========================================================================
|
|
%= Call SimpleCUDD for each BDD Cluster script
|
|
%= L : a list containing 3-tuples a(QueryID,ClusterID,Count)
|
|
%= H : file handle for the log file
|
|
%= P : number of parallel SimpleCUDD processes
|
|
%= T : type of evaluation, either 'd' or 'e'
|
|
%= S : symbol to print after a process finished
|
|
%= OldLLH : accumulator for LLH
|
|
%= LLH : resulting LLH
|
|
%=
|
|
%= evaluate_bdds(+L,+H,+P,+T,+S,+OldLLH,-LLH)
|
|
%========================================================================
|
|
|
|
evaluate_bdds([],_,_,_,_,LLH,LLH).
|
|
evaluate_bdds([H|T],Handle,Parallel_Processes,Type,Symbol,OldLLH,LLH) :-
|
|
once(slice_n([H|T],Parallel_Processes,ForNow,Later)),
|
|
logger_start_timer(bdd_evaluation),
|
|
once(evaluate_bdds_start(ForNow,Type,ForNow_Jobs)),
|
|
once(evaluate_bdds_stop(ForNow_Jobs,Handle,Symbol,OldLLH,NewLLH)),
|
|
logger_stop_timer(bdd_evaluation),
|
|
evaluate_bdds(Later,Handle,Parallel_Processes,Type,Symbol,NewLLH,LLH).
|
|
|
|
evaluate_bdds_start([],_,[]).
|
|
evaluate_bdds_start([a(QueryID,ClusterID,Count)|T],Type,[job(QueryID,ClusterID,Count,Command,PID,OutputFilename)|T2]) :-
|
|
once(update_query(QueryID,ClusterID,Type,Command,PID,OutputFilename)),
|
|
evaluate_bdds_start(T,Type,T2).
|
|
evaluate_bdds_stop([],_,_,LLH,LLH).
|
|
evaluate_bdds_stop([job(ID,ClusterID,Count,Command,PID,OutputFilename)|T],Handle,Symbol,OldLLH,LLH) :-
|
|
once(update_query_wait(ID,ClusterID,Count,Symbol,Command,PID,OutputFilename,BDD_Prob)),
|
|
format(Handle,'bdd_prob(~w,~w,~15e). % Count=~w~n',[ID,ClusterID,BDD_Prob,Count]),
|
|
catch(NewLLH is OldLLH + Count*log(BDD_Prob),_Exception,NewLLH is 0.0/0.0),
|
|
evaluate_bdds_stop(T,Handle,Symbol,NewLLH,LLH).
|
|
|
|
|
|
%========================================================================
|
|
%=
|
|
%=
|
|
%========================================================================
|
|
|
|
|
|
|
|
%========================================================================
|
|
%= initialize the logger module and set the flags for learning
|
|
%= don't change anything here! use set_learning_flag/2 instead
|
|
%========================================================================
|
|
|
|
init_flags :-
|
|
prolog_file_name('queries',Queries_Folder), % get absolute file name for './queries'
|
|
prolog_file_name('output',Output_Folder), % get absolute file name for './output'
|
|
problog_define_flag(bdd_directory, problog_flag_validate_directory, 'directory for BDD scripts', Queries_Folder,learning_general),
|
|
problog_define_flag(output_directory, problog_flag_validate_directory, 'directory for logfiles etc', Output_Folder,learning_general,flags:learning_output_dir_handler),
|
|
problog_define_flag(retain_bdd_output,problog_flag_validate_boolean,'Keep output files from BDD tool',false,learning_general),
|
|
problog_define_flag(log_frequency, problog_flag_validate_posint, 'log results every nth iteration', 1, learning_general),
|
|
problog_define_flag(reuse_initialized_bdds,problog_flag_validate_boolean, 'Reuse BDDs from previous runs',false, learning_general),
|
|
problog_define_flag(pc_numerator,problog_flag_validate_in_interval_right_open([0.0,+inf]),'Add X to numerator (Pseudocounts)',0.0,learning_general),
|
|
problog_define_flag(pc_denominator,problog_flag_validate_in_interval_right_open([0.0,+inf]),'Add X to denominator (Pseudocounts)',0.0,learning_general),
|
|
problog_define_flag(parallel_processes,problog_flag_validate_posint,'Number of parallel BDD processes',8,learning_general),
|
|
|
|
problog_define_flag(cluster_bdds,problog_flag_validate_boolean,'Cluster similar BDDs',true,learning_general).
|
|
|
|
|
|
init_logger :-
|
|
logger_define_variable(iteration, int),
|
|
logger_define_variable(duration,time),
|
|
|
|
logger_define_variable(llh_training_set,float),
|
|
logger_define_variable(llh_test_set,float),
|
|
|
|
logger_define_variable(bdd_evaluation,time),
|
|
|
|
logger_define_variable(ground_truth_diff,float),
|
|
logger_define_variable(ground_truth_mindiff,float),
|
|
logger_define_variable(ground_truth_maxdiff,float),
|
|
|
|
logger_define_variable(train_bdd_script_generation,time),
|
|
logger_define_variable(train_bdd_script_generation_grounding,time),
|
|
logger_define_variable(train_bdd_script_generation_completion,time),
|
|
logger_define_variable(train_bdd_script_generation_propagation,time),
|
|
logger_define_variable(train_bdd_script_generation_splitting,time),
|
|
logger_define_variable(train_bdd_script_generation_active_ground_atoms,int),
|
|
logger_define_variable(train_bdd_script_generation_propagated_ground_atoms,int),
|
|
|
|
logger_define_variable(test_bdd_script_generation,time),
|
|
logger_define_variable(test_bdd_script_generation_grounding,time),
|
|
logger_define_variable(test_bdd_script_generation_completion,time),
|
|
logger_define_variable(test_bdd_script_generation_propagation,time),
|
|
logger_define_variable(test_bdd_script_generation_splitting,time),
|
|
logger_define_variable(test_bdd_script_generation_active_ground_atoms,int),
|
|
logger_define_variable(test_bdd_script_generation_propagated_ground_atoms,int).
|
|
|
|
:- initialization(init_flags).
|
|
:- initialization(init_logger).
|
|
|
|
%:- spy em_one_iteration.
|
|
|
|
|
|
%:- initialization(do_learning(100) ).
|