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%%% -*- Mode: Prolog; -*-
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% $Date: 2011-04-21 14:18:59 +0200 (Thu, 21 Apr 2011) $
% $Revision: 6364 $
%
% This file is part of ProbLog
% http://dtai.cs.kuleuven.be/problog
%
% ProbLog was developed at Katholieke Universiteit Leuven
%
% Copyright 2008, 2009, 2010
% Katholieke Universiteit Leuven
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% Main authors of this file:
% Bernd Gutmann, Vitor Santos Costa
%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
:- module(learning,[do_learning/1,
do_learning/2,
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set_problog_flag/2,
problog_flag/2,
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reset_learning/0
]).
% switch on all the checks to reduce bug searching time
:- style_check(all).
:- yap_flag(unknown,error).
% load modules from the YAP library
:- use_module(library(lists), [member/2,max_list/2, min_list/2, sum_list/2]).
:- use_module(library(system), [file_exists/1, shell/2]).
:- use_module(library(rbtrees)).
% load our own modules
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:- reexport(problog).
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:- use_module('problog/logger').
:- use_module('problog/flags').
:- use_module('problog/os').
:- use_module('problog/print_learning').
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:- use_module('problog/utils_lbdd').
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:- use_module('problog/utils').
:- use_module('problog/tabling').
% used to indicate the state of the system
:- dynamic(values_correct/0).
:- dynamic(learning_initialized/0).
:- dynamic(current_iteration/1).
:- dynamic(example_count/1).
:- dynamic(query_probability_intern/2).
:- dynamic(query_gradient_intern/4).
:- dynamic(last_mse/1).
:- dynamic(query_is_similar/2).
:- dynamic(query_md5/2).
% used to identify queries which have identical proofs
:- dynamic(query_is_similar/2).
:- dynamic(query_md5/3).
:- multifile(user:example/4).
user:example(A,B,C,=) :-
current_predicate(user:example/3),
user:example(A,B,C).
:- multifile(user:test_example/4).
user:test_example(A,B,C,=) :-
current_predicate(user:test_example/3),
user:test_example(A,B,C).
%========================================================================
%= store the facts with the learned probabilities to a file
%========================================================================
save_model:-
current_iteration(Iteration),
create_factprobs_file_name(Iteration,Filename),
export_facts(Filename).
%========================================================================
%= find out whether some example IDs are used more than once
%= if so, complain and stop
%=
%========================================================================
check_examples :-
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Check example IDs
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
(
(user:example(ID,_,_,_), \+ atomic(ID))
->
(
format(user_error,'The example id of training example ~q ',[ID]),
format(user_error,'is not atomic (e.g foo42, 23, bar, ...).~n',[]),
throw(error(examples))
); true
),
(
(user:test_example(ID,_,_,_), \+ atomic(ID))
->
(
format(user_error,'The example id of test example ~q ',[ID]),
format(user_error,'is not atomic (e.g foo42, 23, bar, ...).~n',[]),
throw(error(examples))
); true
),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Check example probabilities
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
(
(user:example(ID,_,P,_), (\+ number(P); P>1 ; P<0))
->
(
format(user_error,'The training example ~q does not have a valid probability value (~q).~n',[ID,P]),
throw(error(examples))
); true
),
(
(user:test_example(ID,_,P,_), (\+ number(P); P>1 ; P<0))
->
(
format(user_error,'The test example ~q does not have a valid probability value (~q).~n',[ID,P]),
throw(error(examples))
); true
),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Check that no example ID is repeated,
% and if it is repeated make sure the query is the same
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
(
(
(
user:example(ID,QueryA,_,_),
user:example(ID,QueryB,_,_),
QueryA \= QueryB
) ;
(
user:test_example(ID,QueryA,_,_),
user:test_example(ID,QueryB,_,_),
QueryA \= QueryB
);
(
user:example(ID,QueryA,_,_),
user:test_example(ID,QueryB,_,_),
QueryA \= QueryB
)
)
->
(
format(user_error,'The example id ~q is used several times.~n',[ID]),
throw(error(examples))
); true
).
%========================================================================
%=
%========================================================================
reset_learning :-
retractall(learning_initialized),
retractall(values_correct),
retractall(current_iteration(_)),
retractall(example_count(_)),
retractall(query_probability_intern(_,_)),
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retractall(query_gradient_intern(_,_,_,_)),
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retractall(last_mse(_)),
retractall(query_is_similar(_,_)),
retractall(query_md5(_,_,_)),
set_problog_flag(alpha,auto),
set_problog_flag(learning_rate,examples),
logger_reset_all_variables.
%========================================================================
%= initialize everything and perform Iterations times gradient descent
%= can be called several times
%= if it is called with an epsilon parameter, it stops when the change
%= in the MSE is smaller than epsilon
%========================================================================
do_learning(Iterations) :-
do_learning(Iterations,-1).
do_learning(Iterations,Epsilon) :-
current_predicate(user:example/4),
!,
integer(Iterations),
number(Epsilon),
Iterations>0,
do_learning_intern(Iterations,Epsilon).
do_learning(_,_) :-
format(user_error,'~n~Error: No training examples specified.~n~n',[]).
do_learning_intern(0,_) :-
!.
do_learning_intern(Iterations,Epsilon) :-
Iterations>0,
init_learning,
current_iteration(CurrentIteration),
retractall(current_iteration(_)),
NextIteration is CurrentIteration+1,
assertz(current_iteration(NextIteration)),
EndIteration is CurrentIteration+Iterations-1,
format_learning(1,'~nIteration ~d of ~d~n',[CurrentIteration,EndIteration]),
logger_set_variable(iteration,CurrentIteration),
logger_start_timer(duration),
mse_testset,
ground_truth_difference,
gradient_descent,
problog_flag(log_frequency,Log_Frequency),
(
( Log_Frequency>0, 0 =:= CurrentIteration mod Log_Frequency)
->
once(save_model);
true
),
update_values,
(
last_mse(Last_MSE)
->
(
retractall(last_mse(_)),
logger_get_variable(mse_trainingset,Current_MSE),
assertz(last_mse(Current_MSE)),
!,
MSE_Diff is abs(Last_MSE-Current_MSE)
); (
logger_get_variable(mse_trainingset,Current_MSE),
assertz(last_mse(Current_MSE)),
MSE_Diff is Epsilon+1
)
),
(
(problog_flag(rebuild_bdds,BDDFreq),BDDFreq>0,0 =:= CurrentIteration mod BDDFreq)
->
(
retractall(values_correct),
retractall(query_is_similar(_,_)),
retractall(query_md5(_,_,_)),
empty_bdd_directory,
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,
% empty_output_directory,
logger_write_header,
format_learning(1,'Initializing everything~n',[]),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 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;
empty_bdd_directory
),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Check, if continuous facts are used.
% if yes, switch to problog_exact
% continuous facts are not supported yet.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% 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
%% ),
succeeds_n_times(user:test_example(_,_,_,_),TestExampleCount),
format_learning(3,'~q test examples~n',[TestExampleCount]),
succeeds_n_times(user:example(_,_,_,_),TrainingExampleCount),
assertz(example_count(TrainingExampleCount)),
format_learning(3,'~q training examples~n',[TrainingExampleCount]),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% set learning rate and alpha
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
(
problog_flag(learning_rate,examples)
->
set_problog_flag(learning_rate,TrainingExampleCount);
true
),
(
problog_flag(alpha,auto)
->
(
(user:example(_,_,P,_),P<1,P>0)
->
set_problog_flag(alpha,1.0);
(
succeeds_n_times((user:example(_,_,P,=),P=:=1.0),Pos_Count),
succeeds_n_times((user:example(_,_,P,=),P=:=0.0),Neg_Count),
Alpha is Pos_Count/Neg_Count,
set_problog_flag(alpha,Alpha)
)
)
),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% build BDD script for every example
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
once(init_queries),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% done
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
assertz(current_iteration(0)),
assertz(learning_initialized),
format_learning(1,'~n',[]).
empty_bdd_directory :-
current_key(_,I),
integer(I),
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recorded(I,bdd(_,_,_),R),
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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 :-
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]);
(
problog_flag(libbdd_init_method,(Query,Bdd,Call)),
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Bdd = bdd(Dir, Tree, MapList),
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once(Call),
rb_new(H0),
maplist_to_hash(MapList, H0, Hash),
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% writeln(Dir:Tree:MapList),
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tree_to_grad(Tree, Hash, [], Grad),
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%% %writeln(Call:Tree),
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recordz(QueryID,bdd(Dir, Grad, MapList),_)
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)
),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% check wether this BDD is similar to another BDD
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
(
problog_flag(check_duplicate_bdds,true)
->
true /* ignore this flag for now */
;
true
),!.
%========================================================================
%= 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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
retractall(query_probability_intern(_,_)),
retractall(query_gradient_intern(_,_,_,_)),
assertz(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;
retractall(query_gradient_intern(QueryID,_,_,_))
).
update_query(QueryID,Symbol,What_To_Update) :-
(
query_is_similar(QueryID,_)
->
% we don't have to evaluate the BDD
format_learning(4,'#',[]);
(
problog_flag(sigmoid_slope,Slope),
((What_To_Update=all;query_is_similar(_,QueryID)) -> Method='g' ; Method='l'),
gradient(QueryID, Method, Slope),
format_learning(4,'~w',[Symbol])
)
).
bind_maplist([]).
bind_maplist([Node-Theta|MapList]) :-
get_prob(Node, ProbFact),
inv_sigmoid(ProbFact, Theta),
bind_maplist(MapList).
%get_prob(Node, Prob) :-
% query_probability(Node,Prob), !.
get_prob(Node, Prob) :-
get_fact_probability(Node,Prob).
gradient(QueryID, l, Slope) :-
/* query_probability(21,6.775948e-01). */
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recorded(QueryID, bdd(Dir, Tree, MapList), _),
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bind_maplist(MapList),
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run_sp(Tree, Slope, 1.0, Prob0),
(Dir == 1 -> Prob0 = Prob ; Prob is 1.0-Prob0),
%writeln(QueryID:Prob),
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assert(query_probability_intern(QueryID,Prob)),
fail.
gradient(_QueryID, l, _).
gradient(QueryID, g, Slope) :-
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recorded(QueryID, bdd(Dir, Tree, MapList), _),
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bind_maplist(MapList),
member(I-_, MapList),
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run_grad(Tree, I, Slope, 0.0, Grad0),
( Dir = 1 -> Grad = Grad0 ; Grad is -Grad0),
% writeln(grad(QueryID:I:Grad)),
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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, X, _Id, PL, _GL, PR, _GR).Tree, Slope, _, PF) :-
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EP = 1.0 / (1.0 + exp(-X * Slope) ),
P is EP*PL+ (1.0-EP)*PR,
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run_sp(Tree, Slope, P, PF).
run_sp(gnoden(P,_G, X, _Id, PL, _GL, PR, _GR).Tree, Slope, _, PF) :-
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EP is 1.0 / (1.0 + exp(-X * Slope) ),
P is EP*PL + (1.0-EP)*(1.0 - PR),
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run_sp(Tree, Slope, P, PF).
run_grad([], _I, _, G0, G0).
run_grad([gnodep(P,G, X, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :-
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EP is 1.0/(1.0 + exp(-X * Slope)),
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 G0+(PL-PR)* EP*(1-EP)*Slope ; G = G0 ),
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run_grad(Tree, I, Slope, G, GF).
run_grad([gnoden(P,G, X, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :-
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EP is 1.0 / (1.0 + exp(-X * Slope) ),
P is EP*PL + (1.0-EP)*(1.0 - PR),
G0 is EP*GL - (1.0 - EP) * GR,
( I == Id -> G is G0+(PL+PR-1)*EP*(1-EP)*Slope ; G = G0 ),
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run_grad(Tree, I, Slope, G, GF).
%========================================================================
%= 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) :-
!,
assertz(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),
assertz(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)
)
).
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query_gradient(QueryID,Fact,p,Value) :- !,
query_gradient_intern(QueryID,Fact,p,Value).
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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) :-
update_values,
example_count(Example_Count),
bb_put(error_train_line_search,0.0),
forall(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))
->
(
bb_get(error_train_line_search,Old_Error),
New_Error is Old_Error + (CurrentProb-QueryProb)**2,
bb_put(error_train_line_search,New_Error)
);true
)
)
),
bb_delete(error_train_line_search,Error),
MSE is Error/Example_Count,
format_learning(3,' (~8f)~n',[MSE]),
retractall(values_correct).
mse_testset :-
current_iteration(Iteration),
create_test_predictions_file_name(Iteration,File_Name),
open(File_Name,'write',Handle),
format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%~n",[]),
format(Handle,"% Iteration, train/test, QueryID, Query, GroundTruth, Prediction %~n",[]),
format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%~n",[]),
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,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 :-
forall(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)
)
)
).
forget_old_probabilities :-
forall(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,_)
)
)
).
add_gradient(Learning_Rate) :-
forall(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),
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%writeln(FactID:OldValue +Learning_Rate*GradValue),
NewValue is OldValue +Learning_Rate*GradValue,
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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)
)
)
),
retractall(values_correct).
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% vsc: avoid silly search
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gradient_descent :-
current_iteration(Iteration),
create_training_predictions_file_name(Iteration,File_Name),
open(File_Name,'write',Handle),
format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%~n",[]),
format(Handle,"% Iteration, train/test, QueryID, Query, GroundTruth, Prediction %~n",[]),
format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%~n",[]),
format_learning(2,'Gradient ',[]),
save_old_probabilities,
update_values,
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% start set gradient to zero
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
forall(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)
)
)
),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 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),
bb_put(llh_training_queries, 0.0),
problog_flag(alpha,Alpha),
logger_set_variable(alpha,Alpha),
example_count(Example_Count),
forall(user:example(QueryID,Query,QueryProb,Type),
(
once(update_query(QueryID,'.',all)),
query_probability(QueryID,BDDProb),
format(Handle,'ex(~q,train,~q,~q,~10f,~10f).~n',[Iteration,QueryID,Query,QueryProb,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),
bb_get(llh_training_queries,Old_LLH_Training_Queries),
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),
New_LLH_Training_Queries is Old_LLH_Training_Queries+log(BDDProb),
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),
bb_put(llh_training_queries,New_LLH_Training_Queries),
( % go over all tunable facts
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query_gradient(QueryID,FactID,p,GradValue),
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!
%writeln(u:QueryID:FactID:Y:GradValue),
bb_get(Key,OldValue),
NewValue is OldValue - Y*GradValue,
bb_put(Key,NewValue),
fail; % go to next fact
true
),
( continuous_fact(FactID),
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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),
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bb_put(Key2,NewValueSigma),
fail
;
true
),
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once(update_query_cleanup(QueryID))
)),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% stop calculate gradient
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
!,
close(Handle),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 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),
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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),
bb_delete(llh_training_queries,LLH_Training_Queries),
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),
logger_set_variable(llh_training_queries,LLH_Training_Queries),
format_learning(2,'~n',[]),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% start add gradient to current probabilities
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
(
problog_flag(line_search,false)
->
problog_flag(learning_rate,LearningRate);
lineSearch(LearningRate,_)
),
format_learning(3,'learning rate:~8f~n',[LearningRate]),
add_gradient(LearningRate),
logger_set_variable(learning_rate,LearningRate),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% stop add gradient to current probabilities
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
!,
forget_old_probabilities.
2012-04-27 15:30:39 +01:00
%========================================================================
%=
%=
%========================================================================
line_search_evaluate_point(Learning_Rate,MSE) :-
add_gradient(Learning_Rate),
format_learning(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)),
format_learning(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 B - Tau*(B-A),
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),
Parameters=ls(A,B,InitLeft,InitRight,Value_A,Value_B,Value_InitLeft,Value_InitRight,1),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% BEGIN BACK TRACKING
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
(
repeat,
Parameters=ls(Ak,Bk,Left,Right,Fl,Fr,FLeft,FRight,Iteration),
(
% 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 Left + Bk - Right,
line_search_evaluate_point(RightNew,FRightNew),
BkNew=Bk,
FrNew=Fr,
Interval_Size is Bk-Left
);
(
BkNew=Right,
FrNew=FRight,
RightNew=Left,
FRightNew=FLeft,
LeftNew is Ak + Right - Left,
line_search_evaluate_point(LeftNew,FLeftNew),
AkNew=Ak,
FlNew=Fl,
Interval_Size is Right-Ak
)
),
Next_Iteration is Iteration + 1,
nb_setarg(9,Parameters,Next_Iteration),
nb_setarg(1,Parameters,AkNew),
nb_setarg(2,Parameters,BkNew),
nb_setarg(3,Parameters,LeftNew),
nb_setarg(4,Parameters,RightNew),
nb_setarg(5,Parameters,FlNew),
nb_setarg(6,Parameters,FrNew),
nb_setarg(7,Parameters,FLeftNew),
nb_setarg(8,Parameters,FRightNew),
% is the search interval smaller than the tolerance level?
Interval_Size<Acc,
% apperantly it is, so get me out of here and
% cut away the choice point from repeat
!
),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% END BACK TRACKING
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 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)
).
%========================================================================
%= initialize the logger module and set the flags for learning
%= don't change anything here! use set_problog_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(log_frequency, problog_flag_validate_posint, 'log results every nth iteration', 1, learning_general),
problog_define_flag(rebuild_bdds, problog_flag_validate_nonegint, 'rebuild BDDs every nth iteration', 0, learning_general),
problog_define_flag(reuse_initialized_bdds,problog_flag_validate_boolean, 'Reuse BDDs from previous runs',false, learning_general),
problog_define_flag(check_duplicate_bdds,problog_flag_validate_boolean,'Store intermediate results in hash table',true,learning_general),
problog_define_flag(libbdd_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),
problog_define_flag(alpha,problog_flag_validate_number,'weight of negative examples (auto=n_p/n_n)',auto,learning_general,flags:auto_handler),
problog_define_flag(sigmoid_slope,problog_flag_validate_posnumber,'slope of sigmoid function',1.0,learning_general),
problog_define_flag(learning_rate,problog_flag_validate_posnumber,'Default learning rate (If line_search=false)',examples,learning_line_search,flags:examples_handler),
problog_define_flag(line_search, problog_flag_validate_boolean,'estimate learning rate by line search',false,learning_line_search),
problog_define_flag(line_search_never_stop, problog_flag_validate_boolean,'make tiny step if line search returns 0',true,learning_line_search),
problog_define_flag(line_search_tau, problog_flag_validate_indomain_0_1_open,'tau value for line search',0.618033988749,learning_line_search),
problog_define_flag(line_search_tolerance,problog_flag_validate_posnumber,'tolerance value for line search',0.05,learning_line_search),
problog_define_flag(line_search_interval, problog_flag_validate_dummy,'interval for line search',(0,100),learning_line_search,flags:linesearch_interval_handler).
init_logger :-
logger_define_variable(iteration, int),
logger_define_variable(duration,time),
logger_define_variable(mse_trainingset,float),
logger_define_variable(mse_min_trainingset,float),
logger_define_variable(mse_max_trainingset,float),
logger_define_variable(mse_testset,float),
logger_define_variable(mse_min_testset,float),
logger_define_variable(mse_max_testset,float),
logger_define_variable(gradient_mean,float),
logger_define_variable(gradient_min,float),
logger_define_variable(gradient_max,float),
logger_define_variable(ground_truth_diff,float),
logger_define_variable(ground_truth_mindiff,float),
logger_define_variable(ground_truth_maxdiff,float),
logger_define_variable(learning_rate,float),
logger_define_variable(alpha,float),
logger_define_variable(llh_training_queries,float),
logger_define_variable(llh_test_queries,float).
:- initialization(init_flags).
:- initialization(init_logger).
