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small fixes

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This commit is contained in:
Vitor Santos Costa
2018-10-07 14:27:01 +01:00
parent ba6e0728ae
commit 6d0b702d1c
8 changed files with 473 additions and 490 deletions
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packages/ProbLog/problog_lbfgs.yap
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@@ -19,61 +19,61 @@
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Artistic License 2.0
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% Copyright (c) 2000-2006, The Perl Foundation.
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% "Package" means the collection of files distributed by the Copyright
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% "Distribute" means providing a copy of the Package or making it
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% "Distributor Fee" means any fee that you charge for Distributing this
% Package or providing support for this Package to another party. It
% does not mean licensing fees.
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% "Standard Version" refers to the Package if it has not been modified,
% or has been modified only in ways explicitly requested by the
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% "Modified Version" means the Package, if it has been changed, and such
% changes were not explicitly requested by the Copyright Holder.
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% "Original License" means this Artistic License as Distributed with the
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@@ -81,34 +81,34 @@
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% Permission for Use and Modification Without Distribution
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% (2) You may Distribute verbatim copies of the Source form of the
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% (3) You may apply any bug fixes, portability changes, and other
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% (4) You may Distribute your Modified Version as Source (either gratis
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@@ -127,7 +127,7 @@
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% Distribution of Compiled Forms of the Standard Version or
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% the Source, provided that you include complete instructions on how to
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@@ -138,13 +138,13 @@
% within thirty days after you become aware that the instructions are
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% Aggregating or Linking the Package
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% (7) You may aggregate the Package (either the Standard Version or
% Modified Version) with other packages and Distribute the resulting
% aggregation provided that you do not charge a licensing fee for the
@@ -152,7 +152,7 @@
% components in the aggregation are permitted. The terms of this license
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% (8) You are permitted to link Modified and Standard Versions with
% other works, to embed the Package in a larger work of your own, or to
% build stand-alone binary or bytecode versions of applications that
@@ -160,7 +160,7 @@
% provided the result does not expose a direct interface to the Package.
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% (9) Works (including, but not limited to, modules and scripts) that
% merely extend or make use of the Package, do not, by themselves, cause
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@@ -168,21 +168,21 @@
% terms of this license.
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% General Provisions
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% (10) Any use, modification, and distribution of the Standard or
% Modified Versions is governed by this Artistic License. By using,
% modifying or distributing the Package, you accept this license. Do not
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% (13) This license includes the non-exclusive, worldwide,
% free-of-charge patent license to make, have made, use, offer to sell,
% sell, import and otherwise transfer the Package with respect to any
@@ -192,7 +192,7 @@
% that the Package constitutes direct or contributory patent
% infringement, then this Artistic License to you shall terminate on the
% date that such litigation is filed.
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% (14) Disclaimer of Warranty: THE PACKAGE IS PROVIDED BY THE COPYRIGHT
% HOLDER AND CONTRIBUTORS "AS IS' AND WITHOUT ANY EXPRESS OR IMPLIED
% WARRANTIES. THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
@@ -308,7 +308,7 @@ check_examples :-
throw(error(examples))
); true
),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Check example probabilities
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -342,7 +342,7 @@ check_examples :-
user:example(ID,QueryB,_,_),
QueryA \= QueryB
) ;
(
user:test_example(ID,QueryA,_,_),
user:test_example(ID,QueryB,_,_),
@@ -362,7 +362,7 @@ check_examples :-
); true
).
%========================================================================
%=
%=
%========================================================================
reset_learning :-
@@ -413,7 +413,7 @@ do_learning_intern(Iterations,Epsilon) :-
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),
@@ -422,7 +422,7 @@ do_learning_intern(Iterations,Epsilon) :-
gradient_descent,
problog_flag(log_frequency,Log_Frequency),
(
( Log_Frequency>0, 0 =:= CurrentIteration mod Log_Frequency)
->
@@ -431,7 +431,7 @@ do_learning_intern(Iterations,Epsilon) :-
),
% update_values,
(
last_mse(Last_MSE)
->
@@ -443,7 +443,7 @@ do_learning_intern(Iterations,Epsilon) :-
MSE_Diff is abs(Last_MSE-Current_MSE)
); (
logger_get_variable(mse_trainingset,Current_MSE),
assertz(last_mse(Current_MSE)),
assertz(last_mse(Current_MSE)),
MSE_Diff is Epsilon+1
)
),
@@ -463,7 +463,7 @@ do_learning_intern(Iterations,Epsilon) :-
!,
logger_stop_timer(duration),
logger_write_data,
@@ -489,17 +489,17 @@ init_learning :-
!.
init_learning :-
check_examples,
% empty_output_directory,
logger_write_header,
format_learning(1,'Initializing everything~n',[]),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Check, if continuous facts are used.
% if yes, switch to problog_exact
% if yes, switch to problog_exact
% continuous facts are not supported yet.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
set_default_gradient_method,
@@ -536,7 +536,7 @@ init_learning :-
format_learning(3,'~q training examples~n',[TrainingExampleCount]),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% build BDD script for every example
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -560,7 +560,7 @@ empty_bdd_directory.
set_default_gradient_method :-
problog_flag(continuous_facts, true),
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',[]),
@@ -612,12 +612,12 @@ init_one_query(QueryID,Query,Type) :-
->
rb_new(H0),
maplist_to_hash(MapList, H0, Hash),
Tree \= [],
tree_to_grad(Tree, Hash, [], Grad)
;
Bdd = bdd(-1,[],[]),
Grad=[]
% ;
% Bdd = bdd(-1,[],[]),
% Grad=[]
),
write('.'),
recordz(QueryID,bdd(Dir, Grad, MapList),_)
;
problog_flag(init_method,(Query,NOf,Bdd,problog_kbest_as_bdd(Call,NOf,Bdd))) ->
@@ -682,7 +682,7 @@ 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, EP, _Id, PL, _GL, PR, _GR).Tree, Slope, _, PF) :-
P is EP*PL+ (1.0-EP)*PR,
@@ -793,7 +793,7 @@ mse_testset :-
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),
@@ -888,10 +888,8 @@ gradient_descent :-
N1 is N-1,
forall(tunable_fact(FactID,GroundTruth), (X[FactID] <== 0.5)),
problog_flag(sigmoid_slope,Slope),
lbfgs_run(Solver,BestFA),
BestF <== BestFA[0],
format('~2nOptimization done~nWe found a minimum ~4f
.~2n',[BestF]),
lbfgs_run(Solver,BestF),
format('~2nOptimization done~nWe found a minimum ~4f.~n',[BestF]),
forall(tunable_fact(FactID,GroundTruth), set_tunable(FactID,GroundTruth,X)),
lbfgs_finalize(Solver).
@@ -917,9 +915,12 @@ bind_maplist([Node-Theta|MapList], Slope, X) :-
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% start calculate gradient
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
user:evaluate(L, X,Grad,N,_,_) :-
user:evaluate(L, X0,Grad,N,_,_) :-
Handle = user_error,
problog_flag(sigmoid_slope,Slope),
forall(between(0,N1,I),
(V is random, X[I] <== V) %, sigmoid(X[I],Slope,Probs[I]) )
)
Probs = X, %<== array[N] of floats,
N1 is N-1,
forall(between(0,N1,I),
@@ -931,7 +932,7 @@ user:evaluate(L, X,Grad,N,_,_) :-
),
sum_list(LLs,LLH_Training_Queries),
forall(tunable_fact(FactID,GroundTruth), (Z<==X[FactID],W<==Grad[FactID])),
L[0] <== LLH_Training_Queries.
L = LLH_Training_Queries.
compute_grad(N, X, Grad, Probs, Slope, Handle, LL) :-
user:example(QueryID,Query,QueryProb,Type),
@@ -957,7 +958,7 @@ gradientpair(_BDD,_Slope,_BDDProb, _Grad).
user:progress(FX,X,_G,X_Norm,G_Norm,Step,_N,Iteration,Ls,0) :-
problog_flag(sigmoid_slope,Slope),
X0 <== X[0],
X1 <== X[1],
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]).
@@ -973,7 +974,7 @@ init_flags :-
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(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),
@@ -1009,4 +1010,3 @@ init_logger :-
:- initialization(init_flags).
:- initialization(init_logger).