Problog

2018-08-31 20:03:00 +01:00
parent e0cc401381
commit f6d8304fcf
9 changed files with 103 additions and 98 deletions
--- a/packages/ProbLog/problog/logger.yap
+++ b/packages/ProbLog/problog/logger.yap
@@ -244,7 +244,7 @@ logger_define_variable(Name,Type) :-
 	->
 		write('redefining logger variable '),write(Name),write(' of type '), write(Type0), nl
 		;
-	throw(error(variable_redefined(logger_define_variable(Name,Type)))
+	throw(error(variable_redefined(logger_define_variable(Name,Type))))
 	).
 logger_define_variable(Name,Type) :-
 	ground(Type),
--- a/packages/ProbLog/problog/ptree.yap
+++ b/packages/ProbLog/problog/ptree.yap
@@ -263,7 +263,7 @@

 % this is a test to determine whether YAP provides the needed trie library
 :- initialization(
-        (       predicate_property(trie_disable_hash, imported_from(tries)) ->
+        (       predicate_property(trie_disable_hash, imported_from(M)) ->
                trie_disable_hash
        ;       print_message(warning,'The predicate tries:trie_disable_hash/0 does not exist. Please update trie library.')
        )
--- a/packages/ProbLog/problog_learning_lbdd.yap
+++ b/packages/ProbLog/problog_learning_lbdd.yap
@@ -389,8 +389,6 @@ reset_learning :-
 do_learning(Iterations) :-
 	do_learning(Iterations,-1).

-:- spy do_learning/2.
-
 do_learning(Iterations,Epsilon) :-
 	current_predicate(user:example/4),
 	!,
@@ -514,32 +512,7 @@ init_learning :-
 	% if yes, switch to problog_exact 
        % continuous facts are not supported yet.
        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-		problog_flag(continuous_facts, true), 
-		!,
-	 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
-	 ),
-	
+	set_default_gradient_method,

 	succeeds_n_times(user:test_example(_,_,_,_),TestExampleCount),
 	format_learning(3,'~q test examples~n',[TestExampleCount]),
@@ -597,6 +570,21 @@ 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 :-
+    set_problog_flag(init_method,(Query,1,BDD,
+	problog_kbest_as_bdd(user:Query,1,BDD))).
+
 %========================================================================
 %= This predicate goes over all training and test examples,
 %= calls the inference method of ProbLog and stores the resulting
@@ -625,29 +613,23 @@ init_one_query(QueryID,Query,Type) :-
 	 format_learning(3,' Reuse existing BDD ~q~n~n',[QueryID]);
 	 (
 	  b_setval(problog_required_keep_ground_ids,false),
-	  problog_flag(libbdd_init_method,(Query,Bdd,Call)),
+	  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,
-	  once(Call),
-	  rb_new(H0),
+	  problog:problog_kbest_as_bdd(user:gene(X,Y),1,Bdd),
 	  maplist_to_hash(MapList, H0, Hash),
 	  Tree \= [],
+	  %put_code(0'.),
 	  writeln(QueryID),
 	  tree_to_grad(Tree, Hash, [], Grad),
 	 recordz(QueryID,bdd(Dir, Grad, MapList),_)
 	 )
-	),
-        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-	% check wether this BDD is similar to another BDD
-	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-	(
-	 problog_flag(check_duplicate_bdds,true)
-	->
-	  true /* ignore this flag for now */
-        ;
-	 true
-	),!.
-init_one_query(_QueryID,_Query,_Type).
+	).
+init_one_query(_QueryID,_Query,_Type) :-
+    throw(unsupported_init_method).



@@ -1006,6 +988,7 @@ inv_sigmoid(T,InvSig) :-

 save_old_probabilities :-
 	problog_flag(continous_facts, true),
+	!,
 	forall(tunable_fact(FactID,_),
 	       (
 		continuous_fact(FactID)
@@ -1024,6 +1007,14 @@ save_old_probabilities :-
 		)
 	       )
 	      ).
+save_old_probabilities :-
+	forall(tunable_fact(FactID,_),
+	       (
+		   get_fact_probability(FactID,OldProbability),
+		   atomic_concat(['old_prob_',FactID],Key),
+		   bb_put(Key,OldProbability)
+	       )
+	      ).



@@ -1106,7 +1097,8 @@ gradient_descent :-
 	
 	save_old_probabilities,
 	update_values,
-	reset_gradients.
+	reset_gradients,
+	compute_gradients(Handle).

 	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 	% start set gradient to zero
@@ -1130,7 +1122,14 @@ gradient_descent :-
 		 bb_put(Key,0.0)
 		)
 	       )
-	      ),
+		      ).
+	reset_gradients :-
+	    forall(tunable_fact(FactID,_),
+	       (
+		 atomic_concat(['grad_',FactID],Key),
+		 bb_put(Key,0.0)
+	       )
+	      ).
 	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 	% stop gradient to zero
 	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -1138,6 +1137,7 @@ gradient_descent :-
 	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 	% start calculate gradient
 	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+compute_gradients(Handle) :-
 	bb_put(mse_train_sum, 0.0),
 	bb_put(mse_train_min, 0.0),
 	bb_put(mse_train_max, 0.0),
@@ -1485,18 +1485,18 @@ my_5_min(V1,V2,V3,V4,V5,F1,F2,F3,F4,F5,VMin,FMin) :-
 %========================================================================

 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'
+	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(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(continuous_facts,problog_flag_validate_boolean,'support parameter learning of continuous distributions',1.0,learning_general),
+	problog_define_flag(continuous_facts,problog_flag_validate_boolean,'support parameter learning of continuous distributions',true,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),
@@ -1504,7 +1504,6 @@ init_flags :-
 	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),
@@ -1527,5 +1526,7 @@ init_logger :-
 	logger_define_variable(llh_test_queries,float).

 :- initialization(init_flags).
+
 :- initialization(init_logger).

+