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problog

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This commit is contained in:
Vítor Santos Costa
2018-10-10 09:39:04 +01:00
parent 9e3a768220
commit 32266a8b98
6 changed files with 83 additions and 49 deletions
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2
packages/ProbLog/problog.yap
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@@ -2613,7 +2613,7 @@ compute_bounds(LP, UP, Status) :-
% same as problog_threshold/5, but lower bound only (no stopped derivations stored)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
problog_low(Goal/Cond, Threshold, _, _) :-
problog_low(Goal/Cond, Threshold, Status, P) :-
!,
problog_low((Cond,Goal), Threshold, P1, Status),
problog_low( Cond, Threshold, P2, Status),

93
packages/ProbLog/problog_lbfgs.yap
View File

@@ -563,7 +563,7 @@ 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 :-
@@ -592,7 +592,7 @@ bdd_input_file(Filename) :-
problog_flag(output_directory,Dir),
concat_path_with_filename(Dir,'input.txt',Filename).
init_one_query(QueryID,Query,Type) :-
init_one_query(QueryID,Query,_Type) :-
% format_learning(3,' ~q example ~q: ~q~n',[Type,QueryID,Query]),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -784,7 +784,7 @@ mse_testset :-
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',[M]).
format_learning(2,' (~8f)~n',[MSE]).
%========================================================================
@@ -819,8 +819,9 @@ save_old_probabilities :-
% vsc: avoid silly search
gradient_descent :-
current_iteration(Iteration),
create_training_predictions_file_name(Iteration,File_Name),
problog_flag(sigmoid_slope,Slope),
% current_iteration(Iteration),
% create_training_predictions_file_name(Iteration,File_Name),
Handle = user_error,
format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%~n",[]),
format(Handle,"% Iteration, train/test, QueryID, Query, GroundTruth, Prediction %~n",[]),
@@ -829,70 +830,89 @@ gradient_descent :-
findall(FactID,tunable_fact(FactID,GroundTruth),L), length(L,N),
% leash(0),trace,
lbfgs_initialize(N,X,0,Solver),
N1 is N-1,
forall(tunable_fact(FactID,GroundTruth), (X[FactID] <== 0.5)),
forall(tunable_fact(FactID,GroundTruth),
(XZ is 0.5, X[FactID] <== XZ,set_fact_probability(FactID,XZ))),
problog_flag(sigmoid_slope,Slope),
%%% lbfgs_set_parameter(min_step, 2e-40, Solver),
lbfgs_run(Solver,BestF),
format('~2nOptimization done~nWe found a minimum ~4f.~n',[BestF]),
forall(tunable_fact(FactID,GroundTruth), set_tunable(FactID,GroundTruth,X)),
forall(tunable_fact(FactID,GroundTruth), set_tunable(FactID,X)),
lbfgs_finalize(Solver).
set_tunable(I, GroundTruth,P) :-
set_tunable(I,P) :-
Pr <== P[I],
get_fact(I,Source),
format('fact(~d, ~q, ~4f, ~4f).~n',[I,Source,GroundTruth,Pr]),
set_fact_probability(I,Pr).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% start calculate gradient
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
user:evaluate(LLH_Training_Queries, X,Grad,N,_,_) :-
Handle = user_error,
%Handle = user_error,
GradCount <== array[N] of ints,
problog_flag(sigmoid_slope,Slope),
Probs = X,
N1 is N-1,
forall(between(0,N1,I),
(Grad[I] <== 0.0) %, sigmoid(X[I],Slope,Probs[I]) )
),
),
findall(LL,
compute_grad(N, X, Grad, Probs, Slope, Handle, LL),
compute_grad(Grad, GradCount, Probs, Slope, LL),
LLs
),
sum_list(LLs,LLH_Training_Queries),
forall(tunable_fact(FactID,GroundTruth), (Z<==X[FactID],W<==Grad[FactID],writeln(FactID:(W->Z)))).
sum_list(LLs,LLH_Training_Queries).
%wrap(X, Grad, GradCount).
compute_grad(N, X, Grad, Probs, Slope, Handle, LL) :-
compute_grad(Grad, GradCount, Probs, Slope, LL) :-
user:example(QueryID,_Query,QueryProb,_),
recorded(QueryID,BDD,_),
BDD = bdd(_Dir, _GradTree, MapList),
bind_maplist(MapList, Slope, Probs),
%writeln( qprobability(BDD,Slope,BDDProb) ),
MapList = [_|_],
bind_maplist(MapList, Slope, Probs),
%writeln( MapList ),
qprobability(BDD,Slope,BDDProb),
%writeln( gradientpair(BDD,Slope,BDDProb, QueryProb, Grad) ),
gradientpair(BDD,Slope,BDDProb, QueryProb, Grad),
LL is (((BDDProb)-(QueryProb))**2).
LL is (((BDDProb)-(QueryProb))**2),
%writeln( qprobability(BDD,Slope,BDDProb) ),
forall(
member(I-_, MapList),
gradientpair(I, BDD,Slope,BDDProb, QueryProb, Grad, GradCount)
).
gradientpair(BDD,Slope,BDDProb, QueryProb, Grad) :-
qgradient(BDD, Slope, FactID, GradValue),
gradientpair(I, BDD,Slope,BDDProb, QueryProb, Grad, GradCount) :-
qgradient(I, BDD, Slope, FactID, GradValue),
% writeln(FactID),
G0 <== Grad[FactID],
%writeln( GN is G0-GradValue*(QueryProb-BDDProb)),
GN is G0-GradValue*(QueryProb-BDDProb),
%writeln(FactID:(G0->GN)),
GN is G0-GradValue*2*(QueryProb-BDDProb),
%writeln(FactID:(G0->GN)),
GC <== GradCount[FactID],
GC1 is GC+1,
GradCount[FactID] <== GC1,
Grad[FactID] <== GN.
gradientpair(_BDD,_Slope,_BDDProb, _Grad).
qprobability(bdd(Dir, Tree, MapList), Slope, Prob) :-
qprobability(bdd(Dir, Tree, _MapList), Slope, Prob) :-
/* query_probability(21,6.775948e-01). */
run_sp(Tree, Slope, 1.0, Prob0),
(Dir == 1 -> Prob0 = Prob ; Prob is 1.0-Prob0).
qgradient(bdd(Dir, Tree, MapList), Slope, I, Grad) :-
member(I-_, MapList),
qgradient(I, bdd(Dir, Tree, _MapList), Slope, I, Grad) :-
run_grad(Tree, I, Slope, 0.0, Grad0),
( Dir = 1 -> Grad = Grad0 ; Grad is -Grad0).
wrap( X, Grad, GradCount) :-
tunable_fact(FactID,GroundTruth),
Z<==X[FactID],
W<==Grad[FactID],
WC<==GradCount[FactID],
WC > 0,
format('ex(~d, ~q, ~4f, ~4f).~n',[FactID,GroundTruth,Z,W]),
% Grad[FactID] <== WN,
fail.
wrap( _X, _Grad, _GradCount).
% writeln(grad(QueryID:I:Grad)),
% assert(query_gradient_intern(QueryID,I,p,Grad)),
% fail.
@@ -931,17 +951,17 @@ run_grad([gnodep(P,G, EP, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :-
P is EP*PL+ (1.0-EP)*PR,
G0 is EP*GL + (1.0-EP)*GR,
% don' t forget the -X
( I == Id -> G is G0+(PL-PR)* EP*(1-EP)*Slope ; G = G0 ),
( I == Id -> G is PL-PR ; G = G0 ),
run_grad(Tree, I, Slope, G, GF).
run_grad([gnoden(P,G, EP, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :-
P is EP*PL + (1.0-EP)*(1.0 - PR),
G0 is EP*GL - (1.0 - EP) * GR,
( I == Id -> G is G0+(PL+PR-1)*EP*(1-EP)*Slope ; G = G0 ),
( I == Id -> G is PL-(1.0-PR) ; G = G0 ),
run_grad(Tree, I, Slope, G, GF).
prob2log(X,Slope,FactID,V) :-
prob2log(_X,Slope,FactID,V) :-
get_fact_probability(FactID, V0),
inv_sigmoid(V0, Slope, V).
@@ -949,17 +969,16 @@ log2prob(X,Slope,FactID,V) :-
V0 <== X[FactID],
sigmoid(V0, Slope, V).
bind_maplist([], Slope, X).
bind_maplist([], _Slope, _X).
bind_maplist([Node-Pr|MapList], Slope, X) :-
V0 <== X[Node],
sigmoid(V0, Slope, V),
Pr <== X[Node],
bind_maplist(MapList, Slope, X).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% stop calculate gradient
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
user:progress(FX,X,_G,X_Norm,G_Norm,Step,_N,Iteration,Ls,0) :-
problog_flag(sigmoid_slope,Slope),
% problog_flag(sigmoid_slope,Slope),
X0 <== X[0],
X1 <== X[1],
format('~d. Iteration : (x0,x1)=(~4f,~4f) f(X)=~4f |X|=~4f |X\'|=~4f Step=~4f Ls=~4f~n',[Iteration,X0 ,X1,FX,X_Norm,G_Norm,Step,Ls]).

2
packages/ProbLog/problog_learning.yap
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@@ -1335,7 +1335,7 @@ lineSearch(Final_X,Final_Value) :-
line_search_evaluate_point(InitLeft,Value_InitLeft),
Parameters=ls(A,B,InitLeft,InitRight,Value_A,Value_B,Value_InitLeft,Value_InitRight,1),
i Parameters=ls(A,B,InitLeft,InitRight,Value_A,Value_B,Value_InitLeft,Value_InitRight,1),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% BEGIN BACK TRACKING