new version of CLP(BN) with EM learning
This commit is contained in:
Vítor Santos de Costa
@@ -2,113 +2,166 @@
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% The world famous EM algorithm, in a nutshell
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%
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:- module(clpbn_em, [em/6]).
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:- module(clpbn_em, [em/5]).
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:- use_module(library(lists),
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[append/3]).
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:- use_module(library(clpbn),
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[clpbn_init_solver/3,
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clpbn_run_solver/3]).
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:- use_module(library('clpbn/dists'),
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[get_dist_domain_size/2,
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empty_dist/2,
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dist_new_table/2]).
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:- use_module(library('clpbn/learning/learn_utils'),
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[run_all/1,
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clpbn_vars/2,
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normalise_counts/2]).
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normalise_counts/2,
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compute_likelihood/3]).
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:- use_module(library(lists),
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[member/2]).
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:- use_module(library(matrix),
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[matrix_add/3,
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matrix_to_list/2]).
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:- use_module(library('clpbn/utils'), [
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check_for_hidden_vars/3]).
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:- meta_predicate em(:,+,+,-,-), init_em(:,-).
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em(Items, MaxError, MaxIts, Tables, Likelihood) :-
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init_em(Items, State),
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em_loop(0, 0.0, state(AllVars,AllDists), MaxError, MaxIts, Likelihood),
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get_tables(State, Tables).
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em_loop(0, 0.0, State, MaxError, MaxIts, Likelihood, Tables).
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% This gets you an initial configuration. If there is a lot of evidence
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% tables may be filled in close to optimal, otherwise they may be
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% close to uniform.
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% it also gets you a run for random variables
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init_em(Items, state(AllVars, AllDists, AllDistInstances)) :-
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% state collects all Info we need for the EM algorithm
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% it includes the list of variables without evidence,
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% the list of distributions for which we want to compute parameters,
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% and more detailed info on distributions, namely with a list of all instances for the distribution.
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init_em(Items, state(AllVars, AllDists, AllDistInstances, MargVars)) :-
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run_all(Items),
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different_dists(AllVars, AllDists, AllDistInstances).
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attributes:all_attvars(AllVars0),
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% remove variables that do not have to do with this query.
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check_for_hidden_vars(AllVars0, AllVars0, AllVars),
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different_dists(AllVars, AllDists, AllDistInstances, MargVars),
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clpbn_init_solver(MargVars, AllVars, _).
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% loop for as long as you want.
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em_loop(MaxIts, Likelihood, State, _, _, MaxIts, Likelihood) :- !.
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em_loop(Its, Likelihood0, State, MaxError, MaxIts, LikelihoodF) :-
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estimate(State),
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maximise(State, Likelihood),
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em_loop(Its, Likelihood0, State, MaxError, MaxIts, LikelihoodF, FTables) :-
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estimate(State, LPs),
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maximise(State, Tables, LPs, Likelihood),
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(
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(
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(
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(Likelihood - Likelihood0)/Likelihood < MaxError
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;
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;
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Its == MaxIts
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)
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)
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->
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ltables(Tables, FTables),
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LikelihoodF = Likelihood
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;
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Its1 is Its+1,
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em_loop(Its1, Likelihood, State, MaxError, MaxIts, LikelihoodF)
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em_loop(Its1, Likelihood, State, MaxError, MaxIts, LikelihoodF, FTables)
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).
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ltables([], []).
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ltables([Id-T|Tables], [Id-LTable|FTables]) :-
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matrix_to_list(T,LTable),
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ltables(Tables, FTables).
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% collect the different dists we are going to learn next.
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different_dists(AllVars, AllDists, AllInfo) :-
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all_dists(AllVars, Dists0, AllInfo),
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different_dists(AllVars, AllDists, AllInfo, MargVars) :-
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all_dists(AllVars, Dists0),
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sort(Dists0, Dists1),
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group(Dists1, AllInfo).
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group(Dists1, AllDists, AllInfo, MargVars, []).
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group([], []).
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group([i(Id,V,Ps)|Dists1], [Id-[[V|Ps]|Extra]|AllInfo]) :-
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same_id(Dists1, Id, Extra, Rest),
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group(Rest, AllInfo).
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all_dists([], []).
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all_dists([V|AllVars], [i(Id, [V|Parents], Cases, Hiddens)|Dists]) :-
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clpbn:get_atts(V, [dist(Id,Parents)]),
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generate_hidden_cases([V|Parents], CompactCases, Hiddens),
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uncompact_cases(CompactCases, Cases),
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all_dists(AllVars, Dists).
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same_id([i(Id,V,Ps)|Dists1], Id, [[V|Ps]|Extra], Rest) :- !,
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same_id(Dists1, Id, Extra, Rest).
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same_id(Dists, _, [], Dists).
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all_dists([], [], []).
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all_dists([V|AllVars], Dists, [i(Id, AllInfo, Parents)|AllInfo]) :-
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generate_hidden_cases([], [], []).
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generate_hidden_cases([V|Parents], [P|Cases], Hiddens) :-
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clpbn:get_atts(V, [evidence(P)]), !,
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generate_hidden_cases(Parents, Cases, Hiddens).
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generate_hidden_cases([V|Parents], [Cases|MoreCases], [V|Hiddens]) :-
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clpbn:get_atts(V, [dist(Id,_)]),
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with_evidence(V, Id, Dists, Dists0), !,
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all_dists(AllVars, Dists0, AllInfo).
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with_evidence(V, Id) -->
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{clpbn:get_atts(V, [evidence(Pos)]) }, !,
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{ dist_pos2bin(Pos, Id, Bin) }.
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with_evidence(V, Id) -->
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[d(V,Id)].
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estimate(state(Vars,Info,_)) :-
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clpbn_solve_graph(Vars, OVars),
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marg_vars(Info, Vars).
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marg_vars([], _).
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marg_vars([d(V,Id)|Vars], AllVs) :-
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clpbn_marginalise_in_vars(V, AllVs),
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marg_vars(Vars, AllVs).
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maximise(state(_,_,DistInstances), Tables, Likelihood) :-
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compute_parameters(DistInstances, Tables, 0.0, Likelihood).
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compute_parameters([], [], Lik, Lik).
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compute_parameters([Id-Samples|Dists], [Tab|Tables], Lik0, Lik) :-
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empty_dist(Id, NewTable),
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add_samples(Samples, NewTable),
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normalise_table(Id, NewTable),
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compute_parameters(Dists, Tables, Lik0, Lik).
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add_samples([], _).
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add_samples([S|Samples], Table) :-
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run_sample(S, 1.0, Pos, Tot),
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matrix_add(Table, Pos, Tot),
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fail.
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add_samples([_|Samples], Table) :-
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add_samples(Samples, Table).
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get_dist_domain_size(Id, Sz),
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gen_cases(0, Sz, Cases),
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generate_hidden_cases(Parents, MoreCases, Hiddens).
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run_sample([], Tot, [], Tot).
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run_sample([V|S], W0, [P|Pos], Tot) :-
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{clpbn:get_atts(V, [evidence(P)]) }, !,
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run_sample(S, W0, Pos, Tot).
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run_sample([V|S], W0, [P|Pos], Tot) :-
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{clpbn_display:get_atts(V, [posterior,(_,_,Ps,_)]) },
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count_cases(Ps, 0, D0, P),
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W1 is D0*W0,
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run_sample(S, W1, Pos, Tot).
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count_cases([D0|Ps], I0, D0, I0).
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count_cases([_|Ps], I0, P, W1) :-
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I is I0+1,
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count_cases(Ps, I, P, W1).
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gen_cases(Sz, Sz, []) :- !.
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gen_cases(I, Sz, [I|Cases]) :-
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I1 is I+1,
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gen_cases(I1, Sz, Cases).
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uncompact_cases(CompactCases, Cases) :-
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findall(Case, is_case(CompactCases, Case), Cases).
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is_case([], []).
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is_case([A|CompactCases], [A|Case]) :-
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integer(A), !,
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is_case(CompactCases, Case).
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is_case([L|CompactCases], [C|Case]) :-
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member(C, L),
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is_case(CompactCases, Case).
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group([], [], []) --> [].
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group([i(Id,Ps,Cs,[])|Dists1], [Id|Ids], [Id-[i(Id,Ps,Cs,[])|Extra]|AllInfo]) --> !,
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same_id(Dists1, Id, Extra, Rest),
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group(Rest, Ids, AllInfo).
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group([i(Id,Ps,Cs,Hs)|Dists1], [Id|Ids], [Id-[i(Id,Ps,Cs,Hs)|Extra]|AllInfo]) -->
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[Hs],
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same_id(Dists1, Id, Extra, Rest),
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group(Rest, Ids, AllInfo).
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same_id([i(Id,Vs,Cases,[])|Dists1], Id, [i(Id, Vs, Cases, [])|Extra], Rest) --> !,
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same_id(Dists1, Id, Extra, Rest).
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same_id([i(Id,Vs,Cases,Hs)|Dists1], Id, [i(Id, Vs, Cases, Hs)|Extra], Rest) --> !,
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[Hs],
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same_id(Dists1, Id, Extra, Rest).
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same_id(Dists, _, [], Dists) --> [].
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estimate(state(Vars, _, _, Margs), LPs) :-
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clpbn_run_solver(Margs, Vars, LPs).
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maximise(state(_,_,DistInstances,_), Tables, LPs, Likelihood) :-
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compute_parameters(DistInstances, Tables, LPs, 0.0, Likelihood).
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compute_parameters([], [], [], Lik, Lik).
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compute_parameters([Id-Samples|Dists], [Id-NewTable|Tables], Ps, Lik0, Lik) :-
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empty_dist(Id, Table0),
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add_samples(Samples, Table0, Ps, MorePs),
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normalise_counts(Table0, NewTable),
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compute_likelihood(Table0, NewTable, DeltaLik),
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dist_new_table(Id, NewTable),
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NewLik is Lik0+DeltaLik,
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compute_parameters(Dists, Tables, MorePs, NewLik, Lik).
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add_samples([], _, Ps, Ps).
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add_samples([i(_,_,[Case],[])|Samples], Table, AllPs, RPs) :- !,
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matrix_add(Table,Case,1.0),
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add_samples(Samples, Table, AllPs, RPs).
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add_samples([i(_,_,Cases,_)|Samples], Table, [Ps|AllPs], RPs) :-
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run_sample(Cases, Ps, Table),
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add_samples(Samples, Table, AllPs, RPs).
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run_sample([], [], _).
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run_sample([C|Cases], [P|Ps], Table) :-
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matrix_add(Table, C, P),
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run_sample(Cases, Ps, Table).
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