diogo/yap-6.3
Archived
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

lbfgs

Browse Source 
memory leaks with _output_
improve configure
This commit is contained in:
Vitor Santos Costa
2018-10-19 14:56:49 +01:00
parent 3786de19de
commit 8fcf3eb008
15 changed files with 731 additions and 660 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
packages/ProbLog/problog_lbdd.yap
View File

@@ -24,6 +24,7 @@ problog_low_lbdd(_, _, Prob, ok, bdd(Dir, Tree, MapList)) :-
bind_maplist(MapList, BoundVars),
bdd_tree(BDD, bdd(Dir, Tree, _Vars)),
bdd_to_probability_sum_product(BDD, BoundVars, Prob),
bdd_close(BDD),
(problog_flag(verbose, true)->
problog_statistics
;
@@ -37,7 +38,8 @@ problog_low_lbdd(_, _, Prob, ok, bdd(Dir, Tree, MapList)) :-
problog_kbest_bdd(Goal, K, Prob, ok) :-
problog_kbest_to_bdd(Goal, K, BDD, MapList),
bind_maplist(MapList, BoundVars),
bdd_to_probability_sum_product(BDD, BoundVars, Prob).
bdd_to_probability_sum_product(BDD, BoundVars, Prob),
bdd_close(BDD).
problog_kbest_as_bdd(Goal, K, bdd(Dir, Tree, MapList)) :-
problog_kbest_to_bdd(Goal, K, BDD, MapList),
@@ -70,6 +72,7 @@ problog_fl_bdd(_,Prob) :-
bind_maplist(MapList, BoundVars),
bdd_to_probability_sum_product(BDD, BoundVars, Prob),
(problog_flag(retain_tables, true) -> retain_tabling; true),
bdd_close(BDD),
clear_tabling.
problog_full_bdd(Goal,_K, _) :-
@@ -86,6 +89,7 @@ problog_full_bdd(_,Prob) :-
tabled_trie_to_bdd(Trie_Completed_Proofs, BDD, MapList),
bind_maplist(MapList, BoundVars),
bdd_to_probability_sum_product(BDD, BoundVars, Prob),
bdd_close(BDD),
(problog_flag(retain_tables, true) -> retain_tabling; true),
clear_tabling.

170
packages/ProbLog/problog_lbfgs.yap
View File

@@ -431,7 +431,7 @@ do_learning_intern(Iterations,Epsilon) :-
true
),
% update_values,
update_values,
(
last_mse(Last_MSE)
@@ -450,15 +450,11 @@ do_learning_intern(Iterations,Epsilon) :-
),
(
(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
),
@@ -491,10 +487,47 @@ init_learning :-
init_learning :-
check_examples,
retractall(current_iteration(_)),
assert(current_iteration(0)),
% empty_output_directory,
logger_write_header,
format_learning(1,'Initializing everything~n',[]),
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]),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% build BDD script for every example
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
once(init_queries),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% done
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
assertz(current_iteration(-1)),
assertz(learning_initialized),
format_learning(1,'~n',[]).
%========================================================================
%= 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 :-
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% delete old values
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
retractall(query_probability_intern(_,_)),
retractall(query_gradient_intern(_,_,_,_)).
@@ -502,8 +535,8 @@ init_learning :-
% Check, if continuous facts are used.
% if yes, switch to problog_exact
% continuous facts are not supported yet.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
set_default_gradient_method,
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
set_default_gradient_method :-
( problog_flag(continuous_facts, true )
->
problog_flag(init_method,(_,_,_,_,OldCall)),
@@ -527,31 +560,11 @@ init_learning :-
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]),
).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% build BDD script for every example
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
once(init_queries),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% done
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
assertz(current_iteration(0)),
assertz(learning_initialized),
format_learning(1,'~n',[]).
empty_bdd_directory :-
empty_bdd_directory :-
current_key(_,I),
integer(I),
recorded(I,bdd(_,_,_),R),
@@ -560,21 +573,6 @@ init_learning :-
empty_bdd_directory.
set_default_gradient_method :-
problog_flag(continuous_facts, true),
!,
% 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 :-
problog_tabled(_), problog_flag(fast_proofs,false),
!,
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.
%========================================================================
%= This predicate goes over all training and test examples,
@@ -584,6 +582,7 @@ set_default_gradient_method.
init_queries :-
empty_bdd_directory,
format_learning(2,'Build BDDs for examples~n',[]),
forall(user:test_example(ID,Query,_Prob,_),init_one_query(ID,Query,test)),
forall(user:example(ID,Query,_Prob,_),init_one_query(ID,Query,training)).
@@ -703,28 +702,56 @@ ground_truth_difference :-
%= -Float
%========================================================================
mse_trainingset_only_for_linesearch(MSE, FX) :-
mse_trainingset :-
current_iteration(Iteration),
create_training_predictions_file_name(Iteration,File_Name),
open(File_Name, write,Handle),
format_learning(2,'MSE_Training ',[]),
update_values,
findall(t(LogCurrentProb,SquaredError),
(user:training_example(QueryID,Query,TrueQueryProb,_Type),
once(update_query(QueryID,'+',probability)),
query_probability(QueryID,CurrentProb),
format(Handle,'ex(~q,training,~q,~q,~10f,~10f).~n',[Iteration,QueryID,Query,TrueQueryProb,CurrentProb]),
example_count(Example_Count),
once(update_query_cleanup(QueryID)),
SquaredError is (CurrentProb-TrueQueryProb)**2,
LogCurrentProb is log(CurrentProb)
),
All),
maplist(tuple, All, AllLogs, AllSquaredErrors),
sum_list( AllLogs, LLH_Training_Queries),
close(Handle),
bb_put(error_train_line_search,0.0),
forall(user:example(QueryID,_Query,QueryProb,Type),
(
once(update_query(QueryID,'.',probability)),
once(update_query_cleanup(QueryID)),
),
format_learning(3,' (~8f)~n',[MSE]),
retractall(values_correct).
length(AllSquaredErrors,Length),
(
Length>0
->
(
sum_list(AllSquaredErrors,SumAllSquaredErrors),
min_list(AllSquaredErrors,MinError),
max_list(AllSquaredErrors,MaxError),
MSE is SumAllSquaredErrors/Length
);(
MSE=0.0,
MinError=0.0,
MaxError=0.0
)
),
logger_set_variable(mse_trainingset,MSE),
logger_set_variable(mse_min_trainingset,MinError),
logger_set_variable(mse_max_trainingset,MaxError),
logger_set_variable(llh_training_queries,LLH_Training_Queries),
format_learning(2,' (~8f)~n',[MSE]).
tuple(t(X,Y),X,Y).
mse_testset :-
current_iteration(Iteration),
create_test_predictions_file_name(Iteration,File_Name),
open(File_Name,'write',Handle),
format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%~n",[]),
format(Handle,"% Iteration, train/test, QueryID, Query, GroundTruth, Prediction %~n",[]),
format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%~n",[]),
open(File_Name, write,Handle),
format_learning(2,'MSE_Test ',[]),
update_values,
bb_put(llh_test_queries,0.0),
@@ -732,7 +759,7 @@ mse_testset :-
(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]),
format(Handle,'ex(~q,test,~q,~q,~10f,~10f).~n',[Iteration,QueryID,Query,TrueQueryProb,CurrentProb]),
once(update_query_cleanup(QueryID)),
(
@@ -808,11 +835,6 @@ save_old_probabilities :-
gradient_descent :-
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",[]),
format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%~n",[]),
findall(FactID,tunable_fact(FactID,GroundTruth),L), length(L,N),
% leash(0),trace,
lbfgs_initialize(N,X,0,Solver),
@@ -839,20 +861,20 @@ user:evaluate(LLH_Training_Queries, X,Grad,N,_,_) :-
N1 is N-1,
forall(between(0,N1,I),
(Grad[I] <== 0.0, S <== X[I], sigmoid(S,Slope, P), Probs[I] <== P)
), nl,
),
forall(
full_example(QueryID,QueryProb,BDD),
compute_grad(QueryID, BDD, QueryProb,Grad, Probs, Slope,LLs)
),
LLH_Training_Queries <== sum(LLs),
writeln(LLH_Training_Queries).
LLH_Training_QueriesSum <== sum(LLs),
LLH_Training_Queries is LLH_Training_QueriesSum/TrainingExampleCount .
%wrap(X, Grad, GradCount).
full_example(QueryID,QueryProb,BDD) :-
user:example(QueryID,_Query,QueryProb,_),
recorded(QueryID,BDD,_),
BDD = bdd(_Dir, _GradTree, MapList),
MapList = [_|_].
BDD = bdd(_Dir, _GradTree, MapList),
MapList = [_|_].
compute_grad(QueryID,BDD,QueryProb, Grad, Probs, Slope, LLs) :-
BDD = bdd(_Dir, _GradTree, MapList),
@@ -863,7 +885,7 @@ compute_grad(QueryID,BDD,QueryProb, Grad, Probs, Slope, LLs) :-
LLs[QueryID] <== LL,
%writeln( qprobability(BDD,Slope,BDDProb) ),
forall(
member(I-_, MapList),
member(I-_, MapList),
gradientpair(I, BDD,Slope,BDDProb, QueryProb, Grad, Probs)
).
@@ -971,7 +993,7 @@ user:progress(FX,X,_G,X_Norm,G_Norm,Step,_N,Iteration,Ls,0) :-
NextIteration is CurrentIteration+1,
assertz(current_iteration(NextIteration)),
save_model,
set_problog_flag(mse_trainingset, FX),
logger_set_variable(mse_trainingset, FX),
X0 <== X[0], sigmoid(X0,Slope,P0),
X1 <== X[1], sigmoid(X1,Slope,P1),
format('~d. Iteration : (x0,x1)=(~4f,~4f) f(X)=~4f |X|=~4f |X\'|=~4f Step=~4f Ls=~4f~n',[Iteration,P0 ,P1,FX,X_Norm,G_Norm,Step,Ls]).