bbdd
This commit is contained in:
Vitor Santos Costa
146
packages/ProbLog/problog/lbdd.yap
Normal file
146
packages/ProbLog/problog/lbdd.yap
Normal file
@@ -0,0 +1,146 @@
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%========================================================================
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%=
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%=
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%=
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%========================================================================
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/**
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* @file problog/lbdd.yap
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* support routines for BDD evaluation.
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*
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*/
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%========================================================================
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%= Updates all values of query_probability/2 and query_gradient/4
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%= should be called always before these predicates are accessed
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%= if the old values are still valid, nothing happens
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%========================================================================
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update_values :-
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values_correct,
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!.
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update_values :-
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\+ values_correct,
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% delete old values
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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retractall(query_probability_intern(_,_)),
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retractall(query_gradient_intern(_,_,_,_)),
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assertz(values_correct).
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update_query_cleanup(QueryID) :-
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(
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(query_is_similar(QueryID,_) ; query_is_similar(_,QueryID))
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->
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% either this query is similar to another or vice versa,
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% therefore we don't delete anything
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true;
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retractall(query_gradient_intern(QueryID,_,_,_))
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).
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update_query(QueryID,Symbol,What_To_Update) :-
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(
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query_is_similar(QueryID,_)
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->
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% we don't have to evaluate the BDD
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format_learning(4,'#',[]);
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(
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problog_flag(sigmoid_slope,Slope),
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((What_To_Update=all;query_is_similar(_,QueryID)) -> Method='g' ; Method='l'),
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gradient(QueryID, Method, Slope),
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format_learning(4,'~w',[Symbol])
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)
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).
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maplist_to_hash([], H0, H0).
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maplist_to_hash([I-V|MapList], H0, Hash) :-
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rb_insert(H0, V, I, H1),
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maplist_to_hash(MapList, H1, Hash).
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bind_maplist([]).
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bind_maplist([Node-Theta|MapList]) :-
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get_prob(Node, ProbFact),
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inv_sigmoid(ProbFact, Theta),
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bind_maplist(MapList).
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tree_to_grad([], _, Grad, Grad).
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tree_to_grad([Node|Tree], H, Grad0, Grad) :-
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node_to_gradient_node(Node, H, GNode),
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tree_to_grad(Tree, H, [GNode|Grad0], Grad).
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%get_prob(Node, Prob) :-
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% query_probability(Node,Prob), !.
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get_prob(Node, Prob) :-
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get_fact_probability(Node,Prob).
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gradient(QueryID, l, Slope) :-
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probability( QueryID, Slope, Prob),
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assert(query_probability_intern(QueryID,Prob)),
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fail.
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gradient(_QueryID, l, _).
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/* query_probability(21,6.775948e-01). */
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gradient(QueryID, g, Slope) :-
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recorded(QueryID, BDD, _),
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query_gradients(BDD,Slope,I,Grad),
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% writeln(grad(QueryID:I:Grad)),
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assert(query_gradient_intern(QueryID,I,p,Grad)),
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fail.
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gradient(QueryID, g, Slope) :-
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gradient(QueryID, l, Slope).
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query_probability( DBDD, Slope, Prob) :-
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DBDD = bdd(Dir, Tree, MapList),
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bind_maplist(MapList),
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run_sp(Tree, Slope, 1.0, Prob0),
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(Dir == 1 -> Prob0 = Prob ; Prob is 1.0-Prob0).
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query_gradients(bdd(Dir, Tree, MapList),Slope,I,Grad) :-
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bind_maplist(MapList),
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member(I-_, MapList),
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run_grad(Tree, I, Slope, 0.0, Grad0),
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( Dir = 1 -> Grad = Grad0 ; Grad is -Grad0).
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node_to_gradient_node(pp(P-G,X,L,R), H, gnodep(P,G,X,Id,PL,GL,PR,GR)) :-
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rb_lookup(X,Id,H),
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(L == 1 -> GL=0, PL=1 ; L == 0 -> GL = 0, PL=0 ; L = PL-GL),
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(R == 1 -> GR=0, PR=1 ; R == 0 -> GR = 0, PR=0 ; R = PR-GR).
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node_to_gradient_node(pn(P-G,X,L,R), H, gnoden(P,G,X,Id,PL,GL,PR,GR)) :-
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rb_lookup(X,Id,H),
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(L == 1 -> GL=0, PL=1 ; L == 0 -> GL = 0, PL=0 ; L = PL-GL),
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(R == 1 -> GR=0, PR=1 ; R == 0 -> GR = 0, PR=0 ; R = PR-GR).
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run_sp([], _, P0, P0).
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run_sp(gnodep(P,_G, X, _Id, PL, _GL, PR, _GR).Tree, Slope, _, PF) :-
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EP = 1.0 / (1.0 + exp(-X * Slope) ),
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P is EP*PL+ (1.0-EP)*PR,
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run_sp(Tree, Slope, P, PF).
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run_sp(gnoden(P,_G, X, _Id, PL, _GL, PR, _GR).Tree, Slope, _, PF) :-
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EP is 1.0 / (1.0 + exp(-X * Slope) ),
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P is EP*PL + (1.0-EP)*(1.0 - PR),
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run_sp(Tree, Slope, P, PF).
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run_grad([], _I, _, G0, G0).
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run_grad([gnodep(P,G, X, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :-
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EP is 1.0/(1.0 + exp(-X * Slope)),
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P is EP*PL+ (1.0-EP)*PR,
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G0 is EP*GL + (1.0-EP)*GR,
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% don' t forget the -X
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( I == Id -> G is G0+(PL-PR)* EP*(1-EP)*Slope ; G = G0 ),
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run_grad(Tree, I, Slope, G, GF).
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run_grad([gnoden(P,G, X, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :-
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EP is 1.0 / (1.0 + exp(-X * Slope) ),
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P is EP*PL + (1.0-EP)*(1.0 - PR),
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G0 is EP*GL - (1.0 - EP) * GR,
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( I == Id -> G is G0+(PL+PR-1)*EP*(1-EP)*Slope ; G = G0 ),
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run_grad(Tree, I, Slope, G, GF).
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@@ -25,8 +25,7 @@ graph2bdd(Query,1,bdd(D,T,Vs)) :-
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graph(X,Y, TrieList, Vs),
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bdd_new(TrieList, C),
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bdd_tree(C, BDD),
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BDD = bdd(D,T,_Vs0),
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writeln(BDD).
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BDD = bdd(D,T,_Vs0).
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:- set_problog_flag(init_method,(Q,N,Bdd,user:graph2bdd(Q,N,Bdd))).
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@@ -421,6 +421,7 @@ do_learning_intern(Iterations,Epsilon) :-
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logger_start_timer(duration),
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% mse_testset,
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% ground_truth_difference,
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%leash(0),trace,
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gradient_descent,
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once(save_model),
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@@ -486,8 +487,8 @@ init_learning :-
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succeeds_n_times(user:example(_,_,_,_),TrainingExampleCount),
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assertz(example_count(TrainingExampleCount)),
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format_learning(3,'~q training examples~n',[TrainingExampleCount]),
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current_probs <== array[TrainingExampleCount ] of floats,
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current_lls <== array[TrainingExampleCount ] of floats,
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%current_probs <== array[TrainingExampleCount ] of floats,
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%current_lls <== array[TrainingExampleCount ] of floats,
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forall(tunable_fact(FactID,_GroundTruth),
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set_fact_probability(FactID,0.5)
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),
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@@ -507,18 +508,6 @@ init_learning :-
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format_learning(1,'~n',[]).
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%========================================================================
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%= Updates all values of query_probability/2 and query_gradient/4
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%= should be called always before these predicates are accessed
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%= if the old values are still valid, nothing happensv
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%========================================================================
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update_values :-
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% delete old values
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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qp <== current_probs.
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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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@@ -586,7 +575,7 @@ init_one_query(QueryID,Query,_Type) :-
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problog_flag(init_method,(Query,N,Bdd,user:graph2bdd(Query,N,Bdd))),
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!,
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b_setval(problog_required_keep_ground_ids,false),
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(QueryID mod 100 =:= 0 -> writeln(QueryID) ; true),
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(QueryID mod 100 =:= 0 ->writeln(QueryID) ; true),
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Bdd = bdd(Dir, Tree,MapList),
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user:graph2bdd(Query,N,Bdd),
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rb_new(H0),
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@@ -792,8 +781,7 @@ inv_sigmoid(T,Slope,InvSig) :-
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%= probabilities of the examples have to be recalculated
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%========================================================================
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save_old_probabilities :-
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old_prob <== p.
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save_old_probabilities.
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% vsc: avoid silly search
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@@ -828,59 +816,56 @@ set_tunable(I,Slope,P) :-
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sigmoid(X,Slope,Pr),
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set_fact_probability(I,Pr).
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:- include(problog/lbdd).
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% start calculate gradient
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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user:evaluate(LLH_Training_Queries, X,Grad,N,_,_) :-
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%Handle = user_error,
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LLs = current_lls,
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Probs = current_probs,
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%Handle = user_error,
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example_count(TrainingExampleCount),
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ExProbs <== array[TrainingExampleCount] of floats,
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LLs <== array[N] of floats,
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Probs <== array[N] of floats,
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problog_flag(sigmoid_slope,Slope),
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N1 is N-1,
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forall(between(0,N1,I),
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(Grad[I] <== 0.0, S <== X[I], sigmoid(S,Slope, P), Probs[I] <== P)
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),
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writeln(e0),
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leash(0),trace,
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forall(
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user:example(QueryID,_Query,QueryProb),
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compute_grad(QueryID, QueryProb,Grad, Probs, Slope,LLs)
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recorded(QueryID,BDD,_),
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compute_probability(BDD,Slope,QueryID,ExProbs)
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),
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writeln(Grad),
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forall(
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user:example(QueryID,_Query,QueryProb),
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compute_gradient(QueryID, QueryProb,Grad, Probs, Slope,LLs)
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),
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trace,
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LLH_Training_Queries <== sum(LLs).
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compute_probability( BDD, Slope, Id, Probs) :-
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query_probability( BDD, Slope, Prob),
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Probs[Id] <== Prob.
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compute_grad(QueryID,QueryProb, Grad, Probs, Slope, LLs) :-
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recorded(QueryID,BDD,_),
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BDD = bdd(_Dir, _GradTree, MapList),
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bind_maplist(MapList, Slope, Probs),
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qprobability(BDD,Slope,BDDProb),
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compute_gradient(QueryID,QueryProb, Grad, Probs, ExProbs, Slope, LLs) :-
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recorded(QueryID,BDD,_),
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BDDProb <== ExProbs[QueryID],
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forall(
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query_gradients(BDD,Slope,I,GradValue),
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gradient_pair(BDDProb, QueryProb, Grad, GradValue, I, Probs)
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),
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LL is (BDDProb-QueryProb)*(BDDProb-QueryProb),
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LLs[QueryID] <== LL,
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forall(
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member(I-_,MapList),
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gradientpair(Slope,BDDProb, QueryProb,Grad,Probs,BDD,I)
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),
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writeln(LL).
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writeln(LL),
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LLs[QueryID] <== LL.
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gradientpair(Slope,BDDProb, QueryProb, Grad, Probs,BDD,I) :-
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qgradient(I, BDD, Slope, FactID, GradValue),
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G0 <== Grad[FactID],
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Prob <== Probs[FactID],
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gradient_pair(BDDProb, QueryProb, Grad, GradValue, I, Probs) :-
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G0 <== Grad[I],
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Prob <== Probs[I],
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GN is G0-GradValue*2*Prob*(1-Prob)*(QueryProb-BDDProb),
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Grad[FactID] <== GN.
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qprobability(bdd(Dir, Tree, _MapList), Slope, Prob) :-
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/* query_probability(21,6.775948e-01). */
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run_sp(Tree, Slope, 1, Prob0),
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(Dir == 1 -> Prob0 = Prob ; Prob is 1.0-Prob0).
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qgradient(I, bdd(Dir,Tree,_), Slope, I, Grad) :-
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run_grad(Tree, I, Slope, 1.0, 0.0, Grad0),
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( Dir = 1 -> Grad = Grad0 ; Grad is -Grad0).
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writeln(G0),
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Grad[I] <== GN.
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wrap( X, Grad, GradCount) :-
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tunable_fact(FactID,GroundTruth),
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@@ -894,52 +879,6 @@ wrap( X, Grad, GradCount) :-
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wrap( _X, _Grad, _GradCount).
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% writeln(grad(QueryID:I:Grad)),
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% assert(query_gradient_intern(QueryID,I,p,Grad)),
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% fail.
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%gradient(QueryID, g, Slope) :-
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% gradient(QueryID, l, Slope).
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maplist_to_hash([], H0, H0).
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maplist_to_hash([I-V|MapList], H0, Hash) :-
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rb_insert(H0, V, I, H1),
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maplist_to_hash(MapList, H1, Hash).
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tree_to_grad([], _, Grad, Grad).
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tree_to_grad([Node|Tree], H, Grad0, Grad) :-
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node_to_gradient_node(Node, H, GNode),
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tree_to_grad(Tree, H, [GNode|Grad0], Grad).
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node_to_gradient_node(pp(P-G,X,L,R), H, gnodep(P,G,X,Id,PL,GL,PR,GR)) :-
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rb_lookup(X,Id,H),
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(L == 1 -> GL=0, PL=1 ; L == 0 -> GL = 0, PL=0 ; L = PL-GL),
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(R == 1 -> GR=0, PR=1 ; R == 0 -> GR = 0, PR=0 ; R = PR-GR).
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node_to_gradient_node(pn(P-G,X,L,R), H, gnoden(P,G,X,Id,PL,GL,PR,GR)) :-
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rb_lookup(X,Id,H),
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(L == 1 -> GL=0, PL=1 ; L == 0 -> GL = 0, PL=0 ; L = PL-GL),
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(R == 1 -> GR=0, PR=1 ; R == 0 -> GR = 0, PR=0 ; R = PR-GR).
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run_sp([], _, P0, P0).
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run_sp(gnodep(P,_G, EP, _Id, PL, _GL, PR, _GR).Tree, Slope, PL, PF) :-
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P is EP*PL+ (1.0-EP)*PR,
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run_sp(Tree, Slope, P, PF).
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run_sp(gnoden(P,_G, EP, _Id, PL, _GL, PR, _GR).Tree, Slope, PL, PF) :-
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P is EP*PL + (1.0-EP)*(1.0 - PR),
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run_sp(Tree, Slope, P, PF).
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run_grad([], _I, _, _, G0, G0).
|
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run_grad([gnodep(P,G, EP, Id, PL, GL, PR, GR)|Tree], I, Slope, PL, GL, GF) :-
|
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P is EP*PL+ (1.0-EP)*PR,
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G0 is EP*GL + (1.0-EP)*GR,
|
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% don' t forget the -X
|
||||
( I == Id -> G is PL-PR ; G = G0 ),
|
||||
run_grad(Tree, I, Slope, P, G, GF).
|
||||
run_grad([gnoden(P,G, EP, Id, PL, GL, PR, GR)|Tree], I, Slope, PL, GL, GF) :-
|
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P is EP*PL + (1.0-EP)*(1.0 - PR),
|
||||
G0 is EP*GL - (1.0 - EP) * GR,
|
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( I == Id -> G is PL-(1.0-PR) ; G = G0 ),
|
||||
run_grad(Tree, I, Slope, P, G, GF).
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||||
|
||||
|
||||
|
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prob2log(_X,Slope,FactID,V) :-
|
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@@ -1023,4 +962,3 @@ init_logger :-
|
||||
:- initialization(init_flags).
|
||||
|
||||
:- initialization(init_logger).
|
||||
|
||||
|
||||
@@ -664,138 +664,6 @@ init_one_query(_QueryID,_Query,_Type) :-
|
||||
|
||||
|
||||
|
||||
%========================================================================
|
||||
%= 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 :-
|
||||
values_correct,
|
||||
!.
|
||||
update_values :-
|
||||
\+ values_correct,
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
% delete old values
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
retractall(query_probability_intern(_,_)),
|
||||
retractall(query_gradient_intern(_,_,_,_)),
|
||||
|
||||
|
||||
assertz(values_correct).
|
||||
|
||||
|
||||
|
||||
%========================================================================
|
||||
%=
|
||||
%=
|
||||
%=
|
||||
%========================================================================
|
||||
|
||||
update_query_cleanup(QueryID) :-
|
||||
(
|
||||
(query_is_similar(QueryID,_) ; query_is_similar(_,QueryID))
|
||||
->
|
||||
% either this query is similar to another or vice versa,
|
||||
% therefore we don't delete anything
|
||||
true;
|
||||
retractall(query_gradient_intern(QueryID,_,_,_))
|
||||
).
|
||||
|
||||
|
||||
update_query(QueryID,Symbol,What_To_Update) :-
|
||||
(
|
||||
query_is_similar(QueryID,_)
|
||||
->
|
||||
% we don't have to evaluate the BDD
|
||||
format_learning(4,'#',[]);
|
||||
(
|
||||
problog_flag(sigmoid_slope,Slope),
|
||||
((What_To_Update=all;query_is_similar(_,QueryID)) -> Method='g' ; Method='l'),
|
||||
gradient(QueryID, Method, Slope),
|
||||
format_learning(4,'~w',[Symbol])
|
||||
)
|
||||
).
|
||||
|
||||
bind_maplist([]).
|
||||
bind_maplist([Node-Theta|MapList]) :-
|
||||
get_prob(Node, ProbFact),
|
||||
inv_sigmoid(ProbFact, Theta),
|
||||
bind_maplist(MapList).
|
||||
|
||||
%get_prob(Node, Prob) :-
|
||||
% query_probability(Node,Prob), !.
|
||||
get_prob(Node, Prob) :-
|
||||
get_fact_probability(Node,Prob).
|
||||
|
||||
gradient(QueryID, l, Slope) :-
|
||||
/* query_probability(21,6.775948e-01). */
|
||||
recorded(QueryID, bdd(Dir, Tree, MapList), _),
|
||||
bind_maplist(MapList),
|
||||
run_sp(Tree, Slope, 1.0, Prob0),
|
||||
(Dir == 1 -> Prob0 = Prob ; Prob is 1.0-Prob0),
|
||||
assert(query_probability_intern(QueryID,Prob)),
|
||||
fail.
|
||||
gradient(_QueryID, l, _).
|
||||
gradient(QueryID, g, Slope) :-
|
||||
recorded(QueryID, bdd(Dir, Tree, MapList), _),
|
||||
bind_maplist(MapList),
|
||||
member(I-_, MapList),
|
||||
run_grad(Tree, I, Slope, 0.0, Grad0),
|
||||
( Dir = 1 -> Grad = Grad0 ; Grad is -Grad0),
|
||||
% writeln(grad(QueryID:I:Grad)),
|
||||
assert(query_gradient_intern(QueryID,I,p,Grad)),
|
||||
fail.
|
||||
gradient(QueryID, g, Slope) :-
|
||||
gradient(QueryID, l, Slope).
|
||||
|
||||
maplist_to_hash([], H0, H0).
|
||||
maplist_to_hash([I-V|MapList], H0, Hash) :-
|
||||
rb_insert(H0, V, I, H1),
|
||||
maplist_to_hash(MapList, H1, Hash).
|
||||
|
||||
tree_to_grad([], _, Grad, Grad).
|
||||
tree_to_grad([Node|Tree], H, Grad0, Grad) :-
|
||||
node_to_gradient_node(Node, H, GNode),
|
||||
tree_to_grad(Tree, H, [GNode|Grad0], Grad).
|
||||
|
||||
node_to_gradient_node(pp(P-G,X,L,R), H, gnodep(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).
|
||||
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, X, _Id, PL, _GL, PR, _GR).Tree, Slope, _, PF) :-
|
||||
EP = 1.0 / (1.0 + exp(-X * Slope) ),
|
||||
P is EP*PL+ (1.0-EP)*PR,
|
||||
run_sp(Tree, Slope, P, PF).
|
||||
run_sp(gnoden(P,_G, X, _Id, PL, _GL, PR, _GR).Tree, Slope, _, PF) :-
|
||||
EP is 1.0 / (1.0 + exp(-X * Slope) ),
|
||||
P is EP*PL + (1.0-EP)*(1.0 - PR),
|
||||
run_sp(Tree, Slope, P, PF).
|
||||
|
||||
run_grad([], _I, _, G0, G0).
|
||||
run_grad([gnodep(P,G, X, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :-
|
||||
EP is 1.0/(1.0 + exp(-X * Slope)),
|
||||
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 ),
|
||||
run_grad(Tree, I, Slope, G, GF).
|
||||
run_grad([gnoden(P,G, X, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :-
|
||||
EP is 1.0 / (1.0 + exp(-X * Slope) ),
|
||||
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 ),
|
||||
run_grad(Tree, I, Slope, G, GF).
|
||||
|
||||
|
||||
|
||||
|
||||
%========================================================================
|
||||
%= This predicate reads probability and gradient values from the file
|
||||
|
||||
Reference in New Issue
Block a user