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

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%
% The world famous EM algorithm, in a nutshell
%
:- module(clpbn_em, [em/5]).
:- use_module(library(lists),
[append/3]).
:- use_module(library(clpbn),
[clpbn_init_graph/1,
clpbn_init_solver/5,
clpbn_run_solver/4,
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clpbn_finalize_solver/1,
clpbn_flag/2]).
:- use_module(library('clpbn/dists'),
[get_dist_domain_size/2,
empty_dist/2,
dist_new_table/2,
get_dist_key/2,
randomise_all_dists/0,
uniformise_all_dists/0]).
:- use_module(library('clpbn/learning/learn_utils'),
[run_all/1,
clpbn_vars/2,
normalise_counts/2,
compute_likelihood/3,
soften_sample/2]).
:- use_module(library(lists),
[member/2]).
:- use_module(library(matrix),
[matrix_add/3,
matrix_to_list/2]).
:- use_module(library(rbtrees),
[rb_new/1,
rb_insert/4,
rb_lookup/3]).
:- use_module(library('clpbn/utils'),
[
check_for_hidden_vars/3,
sort_vars_by_key/3]).
:- meta_predicate em(:,+,+,-,-), init_em(:,-).
em(Items, MaxError, MaxIts, Tables, Likelihood) :-
catch(init_em(Items, State),Error,handle_em(Error)),
em_loop(0, 0.0, State, MaxError, MaxIts, Likelihood, Tables),
clpbn_finalize_solver(State),
assert(em_found(Tables, Likelihood)),
fail.
% get rid of new random variables the easy way :)
em(_, _, _, Tables, Likelihood) :-
retract(em_found(Tables, Likelihood)).
handle_em(error(repeated_parents)) :-
assert(em_found(_, -inf)),
fail.
% This gets you an initial configuration. If there is a lot of evidence
% tables may be filled in close to optimal, otherwise they may be
% close to uniform.
% it also gets you a run for random variables
% state collects all Info we need for the EM algorithm
% it includes the list of variables without evidence,
% the list of distributions for which we want to compute parameters,
% and more detailed info on distributions, namely with a list of all instances for the distribution.
init_em(Items, state( AllDists, AllDistInstances, MargVars, SolverVars)) :-
clpbn_flag(em_solver, Solver),
clpbn_init_graph(Solver),
call_run_all(Items),
% randomise_all_dists,
uniformise_all_dists,
attributes:all_attvars(AllVars0),
sort_vars_by_key(AllVars0,AllVars,[]),
% remove variables that do not have to do with this query.
% check_for_hidden_vars(AllVars1, AllVars1, AllVars),
different_dists(AllVars, AllDists, AllDistInstances, MargVars),
clpbn_init_solver(Solver, MargVars, AllVars, _, SolverVars).
% loop for as long as you want.
em_loop(Its, Likelihood0, State, MaxError, MaxIts, LikelihoodF, FTables) :-
estimate(State, LPs),
maximise(State, Tables, LPs, Likelihood),
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% writeln(Likelihood:Its:Likelihood0:Tables),
(
(
abs((Likelihood - Likelihood0)/Likelihood) < MaxError
;
Its == MaxIts
)
->
ltables(Tables, FTables),
LikelihoodF = Likelihood
;
Its1 is Its+1,
em_loop(Its1, Likelihood, State, MaxError, MaxIts, LikelihoodF, FTables)
).
ltables([], []).
ltables([Id-T|Tables], [Key-LTable|FTables]) :-
matrix_to_list(T,LTable),
get_dist_key(Id, Key),
ltables(Tables, FTables).
% collect the different dists we are going to learn next.
different_dists(AllVars, AllDists, AllInfo, MargVars) :-
all_dists(AllVars, Dists0),
sort(Dists0, Dists1),
group(Dists1, AllDists, AllInfo, MargVars0, []),
sort(MargVars0, MargVars).
all_dists([], []).
all_dists([V|AllVars], [i(Id, [V|Parents], Cases, Hiddens)|Dists]) :-
clpbn:get_atts(V, [dist(Id,Parents)]),
sort([V|Parents], Sorted),
length(Sorted, LengSorted),
length(Parents, LengParents),
(
LengParents+1 =:= LengSorted
->
true
;
throw(error(repeated_parents))
),
generate_hidden_cases([V|Parents], CompactCases, Hiddens),
uncompact_cases(CompactCases, Cases),
all_dists(AllVars, Dists).
generate_hidden_cases([], [], []).
generate_hidden_cases([V|Parents], [P|Cases], Hiddens) :-
clpbn:get_atts(V, [evidence(P)]), !,
generate_hidden_cases(Parents, Cases, Hiddens).
generate_hidden_cases([V|Parents], [Cases|MoreCases], [V|Hiddens]) :-
clpbn:get_atts(V, [dist(Id,_)]),
get_dist_domain_size(Id, Sz),
gen_cases(0, Sz, Cases),
generate_hidden_cases(Parents, MoreCases, Hiddens).
gen_cases(Sz, Sz, []) :- !.
gen_cases(I, Sz, [I|Cases]) :-
I1 is I+1,
gen_cases(I1, Sz, Cases).
uncompact_cases(CompactCases, Cases) :-
findall(Case, is_case(CompactCases, Case), Cases).
is_case([], []).
is_case([A|CompactCases], [A|Case]) :-
integer(A), !,
is_case(CompactCases, Case).
is_case([L|CompactCases], [C|Case]) :-
member(C, L),
is_case(CompactCases, Case).
group([], [], []) --> [].
group([i(Id,Ps,Cs,[])|Dists1], [Id|Ids], [Id-[i(Id,Ps,Cs,[])|Extra]|AllInfo]) --> !,
same_id(Dists1, Id, Extra, Rest),
group(Rest, Ids, AllInfo).
group([i(Id,Ps,Cs,Hs)|Dists1], [Id|Ids], [Id-[i(Id,Ps,Cs,Hs)|Extra]|AllInfo]) -->
[Hs],
same_id(Dists1, Id, Extra, Rest),
group(Rest, Ids, AllInfo).
same_id([i(Id,Vs,Cases,[])|Dists1], Id, [i(Id, Vs, Cases, [])|Extra], Rest) --> !,
same_id(Dists1, Id, Extra, Rest).
same_id([i(Id,Vs,Cases,Hs)|Dists1], Id, [i(Id, Vs, Cases, Hs)|Extra], Rest) --> !,
[Hs],
same_id(Dists1, Id, Extra, Rest).
same_id(Dists, _, [], Dists) --> [].
compact_mvars([], []).
compact_mvars([X1,X2|MargVars], CMVars) :- X1 == X2, !,
compact_mvars([X2|MargVars], CMVars).
compact_mvars([X|MargVars], [X|CMVars]) :- !,
compact_mvars(MargVars, CMVars).
estimate(state(_, _, Margs, SolverState), LPs) :-
clpbn_flag(em_solver, Solver),
clpbn_run_solver(Solver, Margs, LPs, SolverState).
maximise(state(_,DistInstances,MargVars,_), Tables, LPs, Likelihood) :-
rb_new(MDistTable0),
create_mdist_table(MargVars, LPs, MDistTable0, MDistTable),
compute_parameters(DistInstances, Tables, MDistTable, 0.0, Likelihood, LPs:MargVars).
create_mdist_table([],[],MDistTable,MDistTable).
create_mdist_table([Vs|MargVars],[Ps|LPs],MDistTable0,MDistTable) :-
rb_insert(MDistTable0, Vs, Ps, MDistTableI),
create_mdist_table(MargVars, LPs, MDistTableI ,MDistTable).
compute_parameters([], [], _, Lik, Lik, _).
compute_parameters([Id-Samples|Dists], [Id-NewTable|Tables], MDistTable, Lik0, Lik, LPs:MargVars) :-
empty_dist(Id, Table0),
add_samples(Samples, Table0, MDistTable),
soften_sample(Table0, SoftenedTable),
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% matrix:matrix_sum(Table0,TotM),
normalise_counts(SoftenedTable, NewTable),
compute_likelihood(Table0, NewTable, DeltaLik),
dist_new_table(Id, NewTable),
NewLik is Lik0+DeltaLik,
compute_parameters(Dists, Tables, MDistTable, NewLik, Lik, LPs:MargVars).
add_samples([], _, _).
add_samples([i(_,_,[Case],[])|Samples], Table, MDistTable) :- !,
matrix_add(Table,Case,1.0),
add_samples(Samples, Table, MDistTable).
add_samples([i(_,_,Cases,Hiddens)|Samples], Table, MDistTable) :-
rb_lookup(Hiddens, Ps, MDistTable),
run_sample(Cases, Ps, Table),
add_samples(Samples, Table, MDistTable).
run_sample([], [], _).
run_sample([C|Cases], [P|Ps], Table) :-
matrix_add(Table, C, P),
run_sample(Cases, Ps, Table).
call_run_all(Mod:Items) :-
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clpbn_flag(em_solver, pcg), !,
backtrack_run_all(Items, Mod).
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call_run_all(Mod:Items) :-
run_all(Mod:Items).
backtrack_run_all([Item|_], Mod) :-
call(Mod:Item),
fail.
backtrack_run_all([_|Items], Mod) :-
backtrack_run_all(Items, Mod).
backtrack_run_all([], _).