lbfgs
This commit is contained in:
Vitor Santos Costa
@@ -244,7 +244,7 @@ logger_define_variable(Name,Type) :-
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->
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write('redefining logger variable '),write(Name),write(' of type '), write(Type0), nl
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;
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throw(error(variable_redefined(logger_define_variable(Name,Type)))
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throw(error(variable_redefined(logger_define_variable(Name,Type))))
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).
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logger_define_variable(Name,Type) :-
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ground(Type),
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@@ -495,27 +495,14 @@ init_learning :-
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format_learning(1,'Initializing everything~n',[]),
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Delete the BDDs from the previous run if they should
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% not be reused
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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(
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(
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problog_flag(reuse_initialized_bdds,true),
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problog_flag(rebuild_bdds,0)
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)
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->
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true;
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empty_bdd_directory
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),
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Check, if continuous facts are used.
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% if yes, switch to problog_exact
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% continuous facts are not supported yet.
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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problog_flag(continuous_facts, true),
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!,
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( problog_flag(continuous_facts, true )
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->
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problog_flag(init_method,(_,_,_,_,OldCall)),
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(
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(
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@@ -528,6 +515,9 @@ init_learning :-
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set_problog_flag(init_method,(Query,Probability,BDDFile,ProbFile,problog_exact_save(Query,Probability,_Status,BDDFile,ProbFile)))
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);
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true
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)
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;
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true
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),
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(
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@@ -622,10 +612,24 @@ init_one_query(QueryID,Query,Type) :-
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(
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recorded(QueryID, _, _)
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->
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format_learning(3,' Reuse existing BDD ~q~n~n',[QueryID]);
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(
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format_learning(3,' Reuse existing BDD ~q~n~n',[QueryID])
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;
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b_setval(problog_required_keep_ground_ids,false),
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problog_flag(libbdd_init_method,(Query,Bdd,Call)),
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problog_flag(init_method,(Query,N,Bdd,graph2bdd(X,Y,N,Bdd))),
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Query =.. [_,X,Y]
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->
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Bdd = bdd(Dir, Tree, MapList),
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% trace,
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graph2bdd(X,Y,N,Bdd),
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rb_new(H0),
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maplist_to_hash(MapList, H0, Hash),
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Tree \= [],
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writeln(QueryID),
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tree_to_grad(Tree, Hash, [], Grad),
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recordz(QueryID,bdd(Dir, Grad, MapList),_)
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;
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b_setval(problog_required_keep_ground_ids,false),
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problog_flag(init_method,(Query,Bdd,Call)),
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Bdd = bdd(Dir, Tree, MapList),
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% trace,
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once(Call),
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@@ -635,7 +639,6 @@ init_one_query(QueryID,Query,Type) :-
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writeln(QueryID),
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tree_to_grad(Tree, Hash, [], Grad),
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recordz(QueryID,bdd(Dir, Grad, MapList),_)
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)
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),
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% check wether this BDD is similar to another BDD
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@@ -1005,7 +1008,8 @@ inv_sigmoid(T,InvSig) :-
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%========================================================================
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save_old_probabilities :-
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problog_flag(continous_facts, true),
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problog_flag(continous_facts, true),
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!,
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forall(tunable_fact(FactID,_),
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(
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continuous_fact(FactID)
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@@ -1025,9 +1029,19 @@ save_old_probabilities :-
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)
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).
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save_old_probabilities :-
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forall(tunable_fact(FactID,_),
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(
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get_fact_probability(FactID,OldProbability),
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atomic_concat(['old_prob_',FactID],Key),
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bb_put(Key,OldProbability)
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)
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).
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forget_old_probabilities :-
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problog_flag(continous_facts, true),
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!,
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forall(tunable_fact(FactID,_),
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(
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continuous_fact(FactID)
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@@ -1051,7 +1065,20 @@ forget_old_probabilities :-
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)
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).
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forget_old_probabilities :-
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forall(tunable_fact(FactID,_),
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(
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atomic_concat(['old_prob_',FactID],Key),
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atomic_concat(['grad_',FactID],Key2),
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bb_delete(Key,_),
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bb_delete(Key2,_)
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)
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).
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add_gradient(Learning_Rate) :-
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problog_flag(continous_facts, true),
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!,
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forall(tunable_fact(FactID,_),
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(
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continuous_fact(FactID)
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@@ -1091,6 +1118,26 @@ add_gradient(Learning_Rate) :-
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)
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),
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retractall(values_correct).
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add_gradient(Learning_Rate) :-
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forall(tunable_fact(FactID,_),
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(
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atomic_concat(['old_prob_',FactID],Key),
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atomic_concat(['grad_',FactID],Key2),
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bb_get(Key,OldProbability),
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bb_get(Key2,GradValue),
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inv_sigmoid(OldProbability,OldValue),
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%writeln(FactID:OldValue +Learning_Rate*GradValue),
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NewValue is OldValue +Learning_Rate*GradValue,
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sigmoid(NewValue,NewProbability),
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% Prevent "inf" by using values too close to 1.0
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Prob_Secure is min(0.999999999,max(0.000000001,NewProbability)),
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set_fact_probability(FactID,Prob_Secure)
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)
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),
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retractall(values_correct).
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% vsc: avoid silly search
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@@ -1493,21 +1540,21 @@ init_flags :-
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problog_define_flag(rebuild_bdds, problog_flag_validate_nonegint, 'rebuild BDDs every nth iteration', 0, learning_general),
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problog_define_flag(reuse_initialized_bdds,problog_flag_validate_boolean, 'Reuse BDDs from previous runs',false, learning_general),
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problog_define_flag(check_duplicate_bdds,problog_flag_validate_boolean,'Store intermediate results in hash table',true,learning_general),
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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),
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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),
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problog_define_flag(alpha,problog_flag_validate_number,'weight of negative examples (auto=n_p/n_n)',auto,learning_general,flags:auto_handler),
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problog_define_flag(sigmoid_slope,problog_flag_validate_posnumber,'slope of sigmoid function',1.0,learning_general),
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problog_define_flag(continuous_facts,problog_flag_validate_boolean,'support parameter learning of continuous distributions',1.0,learning_general),
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% problog_define_flag(continuous_facts,problog_flag_validate_boolean,'support parameter learning of continuous distributions',1.0,learning_general),
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problog_define_flag(learning_rate,problog_flag_validate_posnumber,'Default learning rate (If line_search=false)',examples,learning_line_search,flags:examples_handler),
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problog_define_flag(line_search, problog_flag_validate_boolean,'estimate learning rate by line search',false,learning_line_search),
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problog_define_flag(line_search_never_stop, problog_flag_validate_boolean,'make tiny step if line search returns 0',true,learning_line_search),
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problog_define_flag(line_search_tau, problog_flag_validate_indomain_0_1_open,'tau value for line search',0.618033988749,learning_line_search),
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writeln(1),
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problog_define_flag(line_search_tolerance,problog_flag_validate_posnumber,'tolerance value for line search',0.05,learning_line_search),
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problog_define_flag(line_search_interval, problog_flag_validate_dummy,'interval for line search',(0,100),learning_line_search,flags:linesearch_interval_handler).
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init_logger :-
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logger_define_variable(iteration, int),
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logger_define_variable(iteration, int),
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logger_define_variable(duration,time),
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logger_define_variable(mse_trainingset,float),
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logger_define_variable(mse_min_trainingset,float),
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@@ -1529,3 +1576,5 @@ init_logger :-
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:- initialization(init_flags).
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:- initialization(init_logger).
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