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yap-6.3/packages/CLPBN/learning/learn_mln_wgts.yap

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%
% generative learning in MLNs:
%
% maximise SUM log Pw(Xi=xi|mb(Xi))
%
% or sum N(X) - p(0|mb)n0 - P(1|mb)n1
%
% note that Pw(X|MB) = P(0|MB) || P(1|MB)
%
%
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:- module(learn_mlns_generative,
[learn_mln_generative/0,
portray_mln/0]).
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:- use_module(library(lists)).
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:- use_module(library(tries)).
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:- use_module(library(maplist)).
:- use_module(library(nb)).
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:- use_module(library(matrix)).
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:- reexport(library(mlns)).
:- reexport(library(pfl)).
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:- use_module(library(lbfgs)).
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:- yap_flag(tabling_mode,local).
:- dynamic diff/4, lit/1, i/2.
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prior_means(_, 0.0).
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prior_dev(_, 1.0).
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learn_mln_generative :-
compile,
optimize.
set_weights :-
retract( mln:mln_w(Id, _) ),
optimizer_get_x( Id, W),
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%writeln(weight:W),
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assert( mln:mln_w(Id, W) ),
fail.
set_weights.
adjust_lprior(Lik0, Lik) :-
Lik0 = Lik, !.
adjust_lprior(Lik0, Lik) :-
findall(I-WI, mln_w(I,WI), WIs),
foldl(add_lprior, WIs, Lik0, Lik).
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add_lprior(Id-WI, Lik0, Lik) :-
prior_means(Id, PM),
prior_dev(Id, PV),
Lik is Lik0 + ((WI-PM)*(WI-PM))/(2*PV*PV).
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likelihood(Lik) :-
S = s(0.0),
% nb_create_accumulator(0.0, Acc),
(
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recorded(i, [Ref|N], _),
peval(Ref, LogP),
%writeln(N*P),
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S = s(V),
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V1 is V+N*LogP,
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nb_setarg(1, S, V1),
% nb_add_to_accumulator( Acc, LogP),
fail
;
% nb_accumulator_value(Acc, Lik)
S = s(Lik0),
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%writeln(lik:Lik0),
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adjust_lprior(Lik0, Lik1),
Lik is -Lik1
).
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derive :-
nb_getval(i2, Mat),
nb_getval(d2, MatD),
matrix_set_all(MatD, 0.0),
recorded(i, [Ref|NI], _),
trie_get_entry(Ref, e(_, Ds, Ps)),
member(n(Id,Occs,DN0,DN1), Ds),
matrix_get(Mat, [Id], N),
matrix_get(MatD, [Id], V),
peval(Ps, P0, P1),
X is Occs*(N-P0*(N+DN0)-P1*(N+DN1)),
%writeln(X is NI*(-P0*(DN0)-P1*(DN1))),
V1 is V-NI*X,
matrix_set(MatD, [Id], V1),
fail.
derive :-
nb_getval(d2, MatD),
mln(Ms),
N1 is Ms-1,
between(0, N1, Id),
matrix_get(MatD, [Id], Sum),
%writeln(d:Id:Sum),
adjust_prior(Sum, Id, NSum),
optimizer_set_g(Id, NSum ),
fail.
derive.
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adjust_prior(Lik0, _, Lik) :-
Lik0 = Lik, !.
adjust_prior(Sum, Id, NSum) :-
mln_w(Id, Wi),
prior_means(Id, PM),
prior_dev(Id, PV),
NSum is Sum+(Wi-PM)/(PV*PV).
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:- dynamic old_fx/1.
old_fx(+inf).
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% This is the call back function which is invoked to report the progress
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% if the last argument is set to anything else than 0, the optimizer will
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% stop right now
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user:progress(FX,X_Norm,G_Norm,Step,_N,Iteration,Ls, Out) :-
( Iteration mod 100 =:= 0 -> atomic_concat([tmp_,Iteration,'.pfl'], File), open( File, write, S), portray_mln(S), close(S) ; true ),
retract(old_fx(FX0)),
( Delta is FX-FX0, abs(Delta/FX) < 0.00001 -> Out = 1 ; Out = 0),
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optimizer_get_x(0,X0),
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assert(old_fx(FX)),
format('/* ~d: w[0]=~10f f(X)=~4f |X|=~4f |X\'|=~4f Step=~4f Ls=~4f */~n',[Iteration,X0,FX,X_Norm,G_Norm,Step,Ls]).
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% This is the call back function which evaluates F and the gradient of F
user:evaluate(FX,_N,_Step) :-
set_weights,
likelihood(FX),
derive.
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init_vars(Ev, Pr) :-
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mln(N),
N1 is N-1,
format('/* We start the search for ~d weights at weight[_]=0 */~2n',[N]),
optimizer_initialize(N, Ev, Pr),
between(0, N1, I),
optimizer_set_x(I,0.0),
fail.
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init_vars(_, _).
output_stat(BestF, Status) :-
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( portray_mln,
fail
;
Lik is -BestF,
format('/* Final likelihood=~f */~n/* LBFGS Status=~w */~n',[Lik,Status])
).
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optimize :-
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init_vars(evaluate, progress),
optimizer_run(BestF,Status),
output_stat(BestF, Status),
optimizer_finalize,
format('~2nOptimization done~n',[]).
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compile :-
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init_compiler,
compile_literals,
fail.
/*
compile :-
recorded(i, [Ref|N], _),
trie_get_entry(Ref, E),
writeln(N:E),
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fail.
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*/
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compile.
init_compiler :-
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mln(HowMany),
D is HowMany+1,
matrix_new(ints, [D], M),
matrix_new(floats, [D], MD),
nb_setval(i2,M),
nb_setval(d2,MD),
collect_literals,
init_trie,
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retractall(p_l(_,_,_,_)),
retractall(lmln:p(_,_,_,_)),
fail.
init_compiler.
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init_trie :-
catch(nb_getval( mln_trie, Trie ), _, fail),
trie_close( Trie ),
eraseall( i ),
fail.
init_trie :-
trie_open( Trie ),
nb_setval( mln_trie , Trie ).
collect_literals :-
mln(ParFactor, _Type, _Els, _G, _DConstraints),
factor(markov, ParFactor, Ks, _, _Phi, _Constraints),
maplist(add_lit, Ks),
fail.
collect_literals.
add_lit(K) :-
functor(K, N, A),
functor(K0, N, A),
( lit(K0) -> true ; assert(lit(K0)) ).
compile_literals :-
lit(K),
functor(K, N, A),
statistics(runtime,_),
format(user_error, '/** grounding ~a/~d.~45+**/~n',[N,A]),
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( evidence(K, 1), % only look at literals with evidence...
% ( ground_lit(K),
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%writeln(k:K),
compile_pw(K)
;
statistics(runtime,[_,T]),
format(user_error, '/** took ~d msec.~45+**/~n',[T]),
fail
).
ground_lit(K) :-
functor(K, _, Ar),
ground_lit(0, Ar, K).
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ground_lit(Ar, Ar, _K).
ground_lit(I0, Ar, K) :-
I is I0+1,
(mln):mln_domain(I, K, G, _A),
user:G,
ground_lit(I, Ar, K).
compile_pw(VId) :-
(evidence(VId, 1) -> P = 1 ; P = 0),
compile(VId, P).
compile(VId, Val) :-
findall(p(FId,W,P0,P1,I0,I1), find_prob(VId, Val, FId, W, P0, P1, I0, I1), Fs),
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(
Fs == [] -> fail
;
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Fs = [p(FId,W,1,1,I0,I1)]
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->
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fail
;
sort(Fs, FsS),
merge_lits(FsS, FsN, Ws),
nb_getval( mln_trie, Trie ),
store( Trie, e(Val, Ws, FsN) )
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).
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store( T , E ) :-
trie_check_entry(T, E, R), !,
recorded(i, [R|I], Ref),
erase(Ref),
I1 is I+1,
recorda(i, [R|I1], _).
store( T , E ) :-
trie_put_entry(T, E, R), !,
recorda(i, [R|1], _).
merge_lits([], [], []).
merge_lits([N*p(F,W,A1,A2,I1,I2), p(F,W,A3,A4,I3,I4)|FsS], FsM, Is) :-
A1 == A3,
A2 == A4,
I1 == I3,
I2 == I4, !,
N1 is N+1,
merge_lits([N1*p(F,W,A3,A4,I3,I4)|FsS], FsM, Is).
merge_lits([p(F,W,A1,A2,I1,I2), p(F,W,A3,A4,I3,I4)|FsS], FsM, Is) :-
A1 == A3,
A2 == A4,
I1 == I3,
I2 == I4, !,
merge_lits([2*p(F,W,A3,A4,I3,I4)|FsS], FsM, Is).
merge_lits([p(F,W,A1,A2,I1,I2) | FsS], [p(F,1,W,A1,A2)|FsM], [n(F,1,I1,I2)|Is]) :-
merge_lits(FsS, FsM, Is).
merge_lits([N*p(F,W,A1,A2,I1,I2) | FsS], [p(F,N,W,A1,A2)|FsM], [n(F,N,I1,I2)|Is]) :-
merge_lits(FsS, FsM, Is).
find_prob(VId, E, ParFactor, W, P0, P1, I0, I1) :-
mln(ParFactor, _, _Type, _, Constraints),
% maplist(call,Constraints),
deletei(Constraints, VId, ConstraintsF, Pol),
maplist(expand_domain(VId-Pol), ConstraintsF),
% all other literals are false
( Pol == (+) ->
P0 = 0, P1 = W,
(E == 1 -> /* we are making this true */
inc(ParFactor),
I0 = -1, I1 = 0
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;
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/* it is false */
I0 = 0, I1 = 1
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)
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;
P0 = W, P1 = 0,
(E == 1 -> /* we are making this false */
I0 = 1, I1 = 0
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;
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/* it is true */
inc(ParFactor),
I0 = 0, I1 = -1
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)
).
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expand_domain(VIdPol, true - Lits) :- !,
maplist( false_literal(VIdPol), Lits).
expand_domain(VIdPol, Dom-Lits) :-
call(user:Dom),
maplist( true_literal(VIdPol), Lits).
% we need to check if we have
% L ; L or L ; -L
% in this case skip or it is always true, so fail.
false_literal(L-(-), L).
false_literal(VId-_, L) :-
evidence(L, 1),
L \= VId.
% L is ground
true_literal(L-(+), L) :- !.
true_literal(VId-_, L) :-
L \= VId,
\+ evidence(L, 1).
deletei([true-Lits|More], K, [true-NLits|More], -) :-
force_delete(Lits, K, NLits).
deletei([true-Lits|More], K, [true-Lits|NMore], -) :- !,
force_delete(More, K, NMore).
deletei(More, K, NMore, +) :-
deletei(More, K, NMore).
deletei([Dom-Lits|More], K, [Dom-NLits|More]) :-
force_delete(Lits, K, NLits).
deletei([DomLits|More], K, [DomLits|NMore]) :-
deletei(More, K, NMore).
force_delete([Elem|List], Elem, List).
force_delete([Head|List], Elem, [Head|Residue]) :-
force_delete(List, Elem, Residue).
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inc(Id) :-
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nb_getval(i2, M),
matrix_inc(M, [Id]).
peval(Ref, P) :-
trie_get_entry(Ref, e(Side, _, Ps)),
foldl2(p_eval, Ps, 0.0, P0, 0.0, P1),
logsum(P0, P1, P01),
( Side == 0 -> P = P0-P01 ; P = P1-P01 ).
peval(Ps, P0, P1) :-
%writeln(p:Ds:Ps),
foldl2(p_eval, Ps, 0.0, AP0, 0.0, AP1),
logsum(AP0, AP1, AP01),
P0 is exp( AP0 - AP01 ),
P1 is 1-P0.
p_eval(p(WId, N, W, P0, P1), AP0, A0, AP1, A1) :-
mln_w(WId, W),
A0 is AP0+N*P0,
A1 is AP1+N*P1.