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problog

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This commit is contained in:
Vitor Santos Costa
2019-03-12 10:51:39 +00:00
parent 9378622d42
commit 4afbc4461c
24 changed files with 324 additions and 280 deletions
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2
packages/ProbLog/problog_examples/learn_graph.pl
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@@ -17,7 +17,7 @@
:- use_module(library(matrix)).
:- use_module(('../problog_learning')).
:- stop_low_level_trace.
%%%%
% background knowledge
%%%%

24
packages/ProbLog/problog_examples/learn_graph_lbdd.pl
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@@ -14,12 +14,21 @@
% will run 20 iterations of learning with default settings
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
:- use_module(library(problog_learning)).
:- use_module('../problog_lbfgs').
%:- set_problog_flag(init_method,(Query,K,Bdd,problog:problog_exact_lbdd(Query,Bdd))).
%:- if(true).
:- use_module('kbgraph').
%%%%
% background knowledge
%%%%
% definition of acyclic path using list of visited nodes
/*:- else.
path(X,Y) :- path(X,Y,[X],_).
path(X,X,A,A).
@@ -37,6 +46,8 @@ edge(X,Y) :- dir_edge(X,Y).
absent(_,[]).
absent(X,[Y|Z]):-X \= Y, absent(X,Z).
:- endif.
*/
%%%%
% probabilistic facts
% - probability represented by t/1 term means learnable parameter
@@ -71,11 +82,11 @@ example(13,path(4,5),0.57).
example(14,path(4,6),0.51).
example(15,path(5,6),0.69).
% some examples for learning from proofs:
example(16,(dir_edge(2,3),dir_edge(2,6),dir_edge(6,5),dir_edge(5,4)),0.032).
example(17,(dir_edge(1,6),dir_edge(2,6),dir_edge(2,3),dir_edge(3,4)),0.168).
example(18,(dir_edge(5,3),dir_edge(5,4)),0.14).
example(19,(dir_edge(2,6),dir_edge(6,5)),0.2).
example(20,(dir_edge(1,2),dir_edge(2,3),dir_edge(3,4)),0.432).
%example(16,(dir_edge(2,3),dir_edge(2,6),dir_edge(6,5),dir_edge(5,4)),0.032).
%example(17,(dir_edge(1,6),dir_edge(2,6),dir_edge(2,3),dir_edge(3,4)),0.168).
%example(18,(dir_edge(5,3),dir_edge(5,4)),0.14).
%example(19,(dir_edge(2,6),dir_edge(6,5)),0.2).
%example(20,(dir_edge(1,2),dir_edge(2,3),dir_edge(3,4)),0.432).
%%%%%%%%%%%%%%
% test examples of form test_example(ID,Query,DesiredProbability)
@@ -98,3 +109,4 @@ test_example(33,path(5,4),0.57).
test_example(34,path(6,4),0.51).
test_example(35,path(6,5),0.69).

183
packages/ProbLog/problog_lbfgs.yap
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@@ -221,6 +221,7 @@
:- use_module(library(system), [file_exists/1, shell/2]).
:- use_module(library(rbtrees)).
:- use_module(library(lbfgs)).
:- reexport(library(matrix)).
% load our own modules
:- reexport(problog).
@@ -485,6 +486,8 @@ init_learning :-
succeeds_n_times(user:example(_,_,_,_),TrainingExampleCount),
assertz(example_count(TrainingExampleCount)),
format_learning(3,'~q training examples~n',[TrainingExampleCount]),
current_probs <== array[TrainingExampleCount ] of floats,
current_lls <== array[TrainingExampleCount ] of floats,
forall(tunable_fact(FactID,_GroundTruth),
set_fact_probability(FactID,0.5)
),
@@ -514,9 +517,7 @@ update_values :-
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% delete old values
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
retractall(query_probability_intern(_,_)),
retractall(query_gradient_intern(_,_,_,_)).
qp <== current_probs.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Check, if continuous facts are used.
@@ -579,71 +580,40 @@ bdd_input_file(Filename) :-
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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
b_setval(problog_required_keep_ground_ids,false),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% if BDD file does not exist, call ProbLog
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
problog_flag(init_method,(Query,N,Bdd,user:graph2bdd(Query,N,Bdd))),
!,
b_setval(problog_required_keep_ground_ids,false),
(QueryID mod 100 =:= 0 -> writeln(QueryID) ; true),
Query =.. [_|Args],
% problog_flag(init_method,(Query,N,Bdd,M:graph2bdd(Args,N,Bdd))),
Bdd = bdd(Dir, Tree,
u3777777777/....777;;;;;;;;;;;;;;;;;;;666666666MapList),
user:graph2bdd(Args,N,Bdd),
rb_new(H0),
Bdd = bdd(Dir, Tree,MapList),
user:graph2bdd(Query,N,Bdd),
rb_new(H0),
maplist_to_hash(MapList, H0, Hash),
tree_to_grad(Tree, Hash, [], Grad),
% ;
% Bdd = bdd(-1,[],[]),
% Grad=[]
write('.'),
write('.'),
recordz(QueryID,bdd(Dir, Grad, MapList),_).
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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
b_setval(problog_required_keep_ground_ids,false),
problog_flag(init_method,(Query,_K,Bdd,Call)),
!,
Bdd = bdd(Dir, Tree, MapList),
% trace,
once(Call),
rb_new(H0),
maplist_to_hash(MapList, H0, Hash),
%Tree \= [],
% writeln(Dir:Tree:MapList),
tree_to_grad(Tree, Hash, [], Grads),
recordz(QueryID,bdd(Dir, Grads, MapList),_).
%========================================================================
%=
@@ -738,6 +708,7 @@ mse_trainingset :-
logger_set_variable(mse_min_trainingset,MinError),
logger_set_variable(mse_max_trainingset,MaxError),
logger_set_variable(llh_training_queries,LLH_Training_Queries),
%%%%% format(' (~8f)~n',[MSE]).
format_learning(2,' (~8f)~n',[MSE]).
tuple(t(X,Y),X,Y).
@@ -831,7 +802,6 @@ gradient_descent :-
% 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),
set_fact( FactID, Slope, X)
@@ -861,59 +831,55 @@ set_tunable(I,Slope,P) :-
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 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,
user:evaluate(LLH_Training_Queries, X,Grad,N,_,_) :-
%Handle = user_error,
LLs = current_lls,
Probs = current_probs,
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)
),
writeln(e0),
leash(0),trace,
forall(
full_example(QueryID,QueryProb,BDD),
compute_grad(QueryID, BDD, QueryProb,Grad, Probs, Slope,LLs)
user:example(QueryID,_Query,QueryProb),
compute_grad(QueryID, QueryProb,Grad, Probs, Slope,LLs)
),
writeln(Grad),
LLH_Training_Queries <== sum(LLs).
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) :-
compute_grad(QueryID,QueryProb, Grad, Probs, Slope, LLs) :-
recorded(QueryID,BDD,_),
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)
).
member(I-_,MapList),
gradientpair(Slope,BDDProb, QueryProb,Grad,Probs,BDD,I)
),
writeln(LL).
gradientpair(I, BDD,Slope,BDDProb, QueryProb, Grad, Probs) :-
qgradient(I, BDD, Slope, FactID, GradValue),
% writeln(FactID),
gradientpair(Slope,BDDProb, QueryProb, Grad, Probs,BDD,I) :-
qgradient(I, BDD, Slope, FactID, GradValue),
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.
GN is G0-GradValue*2*Prob*(1-Prob)*(QueryProb-BDDProb),
Grad[FactID] <== GN.
qprobability(bdd(Dir, Tree, _MapList), Slope, Prob) :-
/* query_probability(21,6.775948e-01). */
run_sp(Tree, Slope, 1.0, Prob0),
run_sp(Tree, Slope, 1, 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),
qgradient(I, bdd(Dir,Tree,_), Slope, I, Grad) :-
run_grad(Tree, I, Slope, 1.0, 0.0, Grad0),
( Dir = 1 -> Grad = Grad0 ; Grad is -Grad0).
wrap( X, Grad, GradCount) :-
@@ -954,25 +920,25 @@ node_to_gradient_node(pn(P-G,X,L,R), H, gnoden(P,G,X,Id,PL,GL,PR,GR)) :-
(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) :-
run_sp(gnodep(P,_G, EP, _Id, PL, _GL, PR, _GR).Tree, Slope, PL, 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(gnoden(P,_G, EP, _Id, PL, _GL, PR, _GR).Tree, Slope, PL, 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) :-
run_grad([], _I, _, _, G0, G0).
run_grad([gnodep(P,G, EP, Id, PL, GL, PR, GR)|Tree], I, Slope, PL, GL, 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) :-
run_grad(Tree, I, Slope, P, G, GF).
run_grad([gnoden(P,G, EP, Id, PL, GL, PR, GR)|Tree], I, Slope, PL, GL, 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).
run_grad(Tree, I, Slope, P, G, GF).
@@ -986,7 +952,7 @@ log2prob(X,Slope,FactID,V) :-
bind_maplist([], _Slope, _X).
bind_maplist([Node-Pr|MapList], Slope, X) :-
Pr <== X[Node],
Pr <== X[Node],
bind_maplist(MapList, Slope, X).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -996,7 +962,7 @@ user:progress(FX,_X,_G, _X_Norm,_G_Norm,_Step,_N,_Iteration,_Ls,-1) :-
FX < 0, !,
format('stopped on bad FX=~4f~n',[FX]).
user:progress(FX,X,_G,X_Norm,G_Norm,Step,_N,_Iteration,Ls,0) :-
problog_flag(sigmoid_slope,Slope),
roblog_flag(sigmoid_slope,Slope),
forall(tunable_fact(FactID,_GroundTruth), set_tunable(FactID,Slope,X)),
current_iteration(CurrentIteration),
retractall(current_iteration(_)),
@@ -1015,14 +981,14 @@ user:progress(FX,X,_G,X_Norm,G_Norm,Step,_N,_Iteration,Ls,0) :-
%========================================================================
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),
% 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(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),
@@ -1057,3 +1023,4 @@ init_logger :-
:- initialization(init_flags).
:- initialization(init_logger).

57
packages/ProbLog/problog_learning_lbdd.yap
View File

@@ -70,7 +70,7 @@
% "Original License" means this Artistic License as Distributed with the
% Standard Version of the Package, in its current version or as it may
% be modified by The Perl Foundation in the future.
%
% "Source" form means the source code, documentation source, and
% configuration files for the Package.
%
@@ -587,7 +587,7 @@ empty_bdd_directory.
set_default_gradient_method :-
problog_flag(continuous_facts, true),
!,
problog_flag(init_method,OldMethod),
problog_flag(init_method,_OldMethod),
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))).
set_default_gradient_method :-
@@ -595,9 +595,10 @@ set_default_gradient_method :-
!,
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))).
set_default_gradient_method :-
problog_flag(init_method,(gene(X,Y),N,Bdd,graph2bdd(X,Y,N,Bdd))),
/*set_default_gradient_method :-
problog_flag(init_method,(Goal,N,Bdd,graph2bdd(X,Y,N,Bdd))),
!.
*/
set_default_gradient_method :-
set_problog_flag(init_method,(Query,1,BDD,
problog_kbest_as_bdd(user:Query,1,BDD))).
@@ -618,24 +619,36 @@ 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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
b_setval(problog_required_keep_ground_ids,false),
problog_flag(libbdd_init_method,(Query,Bdd,Call)),
!,
Bdd = bdd(Dir, Tree, MapList),
% trace,
once(Call),
rb_new(H0),
maplist_to_hash(MapList, H0, Hash),
Tree \= [],
% writeln(Dir:Tree:MapList),
tree_to_grad(Tree, Hash, [], Grad).
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),
problog_flag(init_method,(Query,N,Bdd,graph2bdd(X,Y,N,Bdd))),
Query =.. [_,X,Y]
->
problog_flag(init_method,(Query,N,Bdd,_)),
!,
Bdd = bdd(Dir, Tree, MapList),
(
graph2bdd(X,Y,N,Bdd)
user:graph2bdd(Query,N,Bdd)
->
rb_new(H0),
maplist_to_hash(MapList, H0, Hash),
@@ -645,22 +658,7 @@ init_one_query(QueryID,Query,Type) :-
Bdd = bdd(-1,[],[]),
Grad=[]
),
recordz(QueryID,bdd(Dir, Grad, MapList),_)
;
b_setval(problog_required_keep_ground_ids,false),
rb_new(H0),
problog_flag(init_method,(Query,NOf,Bdd,problog_kbest_as_bdd(Call,1,Bdd))),
strip_module(Call,_,gene(X,Y)),
!,
Bdd = bdd(Dir, Tree, MapList),
% trace,
problog:problog_kbest_as_bdd(user:gene(X,Y),1,Bdd),
maplist_to_hash(MapList, H0, Hash),
Tree \= [],
%put_code(0'.),
tree_to_grad(Tree, Hash, [], Grad),
recordz(QueryID,bdd(Dir, Grad, MapList),_)
).
recordz(QueryID,bdd(Dir, Grad, MapList),_).
init_one_query(_QueryID,_Query,_Type) :-
throw(unsupported_init_method).
@@ -1568,6 +1566,7 @@ init_flags :-
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(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),