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CLL->Score

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This commit is contained in:
Fabrizio Riguzzi 2013-09-09 12:41:42 +02:00
parent 52caac6548
commit 700cd2d9dc
1 changed files with 22 additions and 23 deletions
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45
packages/cplint/slipcover/slipcover.pl
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@ -86,16 +86,16 @@ sl(File):-
), ),
% write('Initial theory'),nl, % write('Initial theory'),nl,
% write_rules(R1,user_output), % write_rules(R1,user_output),
learn_struct(DB,R1,R2,CLL2), learn_struct(DB,R1,R2,Score2),
learn_params(DB,R2,R,CLL), learn_params(DB,R2,R,Score),
statistics(walltime,[_,WT]), statistics(walltime,[_,WT]),
WTS is WT/1000, WTS is WT/1000,
format("~nRefinement CLL ~f - CLL after EMBLEM ~f~n",[CLL2,CLL]), format("~nRefinement score ~f - score after EMBLEM ~f~n",[Score2,Score]),
format("Total execution time ~f~n~n",[WTS]), format("Total execution time ~f~n~n",[WTS]),
write_rules(R,user_output), write_rules(R,user_output),
listing(setting/2), listing(setting/2),
open(FileOut,write,Stream), open(FileOut,write,Stream),
format(Stream,'/* SLIPCASE Final CLL ~f~n',[CLL]), format(Stream,'/* SLIPCOVER Final score ~f~n',[Score]),
format(Stream,'Execution time ~f~n',[WTS]), format(Stream,'Execution time ~f~n',[WTS]),
tell(Stream), tell(Stream),
listing(setting/2), listing(setting/2),
@ -111,7 +111,7 @@ gen_fixed([(H,B,BL)|T],[rule(R,H,B,BL)|T1]):-
get_next_rule_number(R), get_next_rule_number(R),
gen_fixed(T,T1). gen_fixed(T,T1).
learn_struct_only(DB,R1,R,CLL):- %+R1:initial theory of the form [rule(NR,[h],[b]],...], -R:final theory of the same form, -CLL learn_struct_only(DB,R1,R,Score):- %+R1:initial theory of the form [rule(NR,[h],[b]],...], -R:final theory of the same form, -CLL
format("Clause search~n~n",[]), format("Clause search~n~n",[]),
setting(max_iter,M), setting(max_iter,M),
setting(depth_bound,DepthB), setting(depth_bound,DepthB),
@ -126,7 +126,7 @@ learn_struct_only(DB,R1,R,CLL):- %+R1:initial theory of the form [rule(NR,[h],
set(depth_bound,DepthB), set(depth_bound,DepthB),
format("Theory search~n~n",[]), format("Theory search~n~n",[]),
setting(max_iter_structure,MS), setting(max_iter_structure,MS),
cycle_structure(TCL,[HCL],S,-inf,DB,R2,CLL,MS), cycle_structure(TCL,[HCL],S,-inf,DB,R2,Score,MS),
format("Best target theory~n~n",[]), format("Best target theory~n~n",[]),
write_rules(R2,user_output), write_rules(R2,user_output),
length(BG,NBG), length(BG,NBG),
@ -135,7 +135,7 @@ learn_struct_only(DB,R1,R,CLL):- %+R1:initial theory of the form [rule(NR,[h],
append(R2,BG2,R). append(R2,BG2,R).
learn_struct(DB,R1,R,CLL):- %+R1:initial theory of the form [rule(NR,[h],[b]],...], -R:final theory of the same form, -CLL learn_struct(DB,R1,R,Score):- %+R1:initial theory of the form [rule(NR,[h],[b]],...], -R:final theory of the same form, -CLL
format("Clause search~n~n",[]), format("Clause search~n~n",[]),
setting(max_iter,M), setting(max_iter,M),
setting(depth_bound,DepthB), setting(depth_bound,DepthB),
@ -150,7 +150,7 @@ learn_struct(DB,R1,R,CLL):- %+R1:initial theory of the form [rule(NR,[h],[b]],
set(depth_bound,DepthB), set(depth_bound,DepthB),
format("Theory search~n~n",[]), format("Theory search~n~n",[]),
setting(max_iter_structure,MS), setting(max_iter_structure,MS),
cycle_structure(TCL,[HCL],S,-inf,DB,R2,CLL,MS), cycle_structure(TCL,[HCL],S,-inf,DB,R2,Score,MS),
format("Best target theory~n~n",[]), format("Best target theory~n~n",[]),
write_rules(R2,user_output), write_rules(R2,user_output),
setting(background_clauses,NBG1), setting(background_clauses,NBG1),
@ -183,7 +183,7 @@ cycle_structure([(RH,_CLL)|RT],R0,S0,SP0,DB,R,S,M):-
format("Theory iteration ~d",[M]),nl,nl, format("Theory iteration ~d",[M]),nl,nl,
write('Already scored, updated refinement'),nl, write('Already scored, updated refinement'),nl,
write_rules(R3,user_output), write_rules(R3,user_output),
write('Score (CLL) '),write(Score),nl,nl,nl, write('Score '),write(Score),nl,nl,nl,
(Score>S0-> (Score>S0->
R4=R3, R4=R3,
S4=Score, S4=Score,
@ -196,7 +196,7 @@ cycle_structure([(RH,_CLL)|RT],R0,S0,SP0,DB,R,S,M):-
M1 is M-1, M1 is M-1,
cycle_structure(RT,R4,S4,SP1,DB,R,S,M1). cycle_structure(RT,R4,S4,SP1,DB,R,S,M1).
cycle_structure([(RH,_CLL)|RT],R0,S0,SP0,DB,R,S,M):- cycle_structure([(RH,_Score)|RT],R0,S0,SP0,DB,R,S,M):-
format("Theory iteration ~d",[M]),nl,nl, format("Theory iteration ~d",[M]),nl,nl,
generate_clauses([RH|R0],R2,0,[],Th1), generate_clauses([RH|R0],R2,0,[],Th1),
format("Initial theory~n~n",[]), format("Initial theory~n~n",[]),
@ -218,17 +218,17 @@ cycle_structure([(RH,_CLL)|RT],R0,S0,SP0,DB,R,S,M):-
), ),
setting(random_restarts_number,N), setting(random_restarts_number,N),
format("~nInitial CLL ~f~n~n",[CLL0]), format("~nInitial CLL ~f~n~n",[CLL0]),
random_restarts(N,Nodes,CLL0,CLL,initial,Par,LE), %output:CLL,Par random_restarts(N,Nodes,CLL0,Score,initial,Par,LE), %output:CLL,Par
format("CLL after EMBLEM = ~f~n",[CLL]), format("Score after EMBLEM = ~f~n",[Score]),
retract_all(Th1), retract_all(Th1),
retract_all(R2),!, retract_all(R2),!,
end, end,
update_theory(R2,Par,R3), update_theory(R2,Par,R3),
write('updated Theory'),nl, write('updated Theory'),nl,
write_rules(R3,user_output), %definite rules without probabilities in the head are not written write_rules(R3,user_output), %definite rules without probabilities in the head are not written
(CLL>S0-> (Score>S0->
R4=R3, R4=R3,
S4=CLL, S4=Score,
SP1=S0, SP1=S0,
write('New best score'),nl write('New best score'),nl
; ;
@ -236,7 +236,7 @@ cycle_structure([(RH,_CLL)|RT],R0,S0,SP0,DB,R,S,M):-
S4=S0, S4=S0,
SP1=SP0 SP1=SP0
), ),
store_refinement([RH|R0],R3,CLL), store_refinement([RH|R0],R3,Score),
M1 is M-1, M1 is M-1,
cycle_structure(RT,R4,S4,SP1,DB,R,S,M1). cycle_structure(RT,R4,S4,SP1,DB,R,S,M1).
@ -259,15 +259,15 @@ em(File):-
set(compiling,off), set(compiling,off),
set(verbosity,3), set(verbosity,3),
statistics(walltime,[_,_]), statistics(walltime,[_,_]),
learn_params(DB,R0,R,CLL), learn_params(DB,R0,R,Score),
statistics(walltime,[_,CT]), statistics(walltime,[_,CT]),
CTS is CT/1000, CTS is CT/1000,
format("EM: Final CLL ~f~n",[CLL]), format("EM: Final score ~f~n",[Score]),
format("Execution time ~f~n~n",[CTS]), format("Execution time ~f~n~n",[CTS]),
write_rules(R,user_output), write_rules(R,user_output),
listing(setting/2), listing(setting/2),
open(FileOut,write,Stream), open(FileOut,write,Stream),
format(Stream,'/* EMBLEM Final CLL ~f~n',[CLL]), format(Stream,'/* EMBLEM Final score ~f~n',[Score]),
format(Stream,'Execution time ~f~n',[CTS]), format(Stream,'Execution time ~f~n',[CTS]),
tell(Stream), tell(Stream),
listing(setting/2), listing(setting/2),
@ -277,7 +277,7 @@ em(File):-
write_rules(R,Stream1), write_rules(R,Stream1),
close(Stream1). close(Stream1).
learn_params(DB,R0,R,CLL):- %Parameter Learning learn_params(DB,R0,R,Score):- %Parameter Learning
generate_clauses(R0,R1,0,[],Th0), generate_clauses(R0,R1,0,[],Th0),
assert_all(Th0), assert_all(Th0),
assert_all(R1),!, assert_all(R1),!,
@ -295,7 +295,7 @@ learn_params(DB,R0,R,CLL):- %Parameter Learning
derive_bdd_nodes(DB,NEx,[],Nodes,0,_CLL0),! derive_bdd_nodes(DB,NEx,[],Nodes,0,_CLL0),!
), ),
setting(random_restarts_number,N), setting(random_restarts_number,N),
random_restarts(N,Nodes,-inf,CLL,initial,Par,LE), %computes new parameters Par random_restarts(N,Nodes,-inf,Score,initial,Par,LE), %computes new parameters Par
end, end,
retract_all(Th0), retract_all(Th0),
retract_all(R1),!, retract_all(R1),!,
@ -368,7 +368,7 @@ score_clause_refinements([R1|T],Nrev,NRef,DB,NB0,NB,CL0,CL,CLBG0,CLBG):- %scans
format('Score ref. ~d of ~d~n',[Nrev,NRef]), format('Score ref. ~d of ~d~n',[Nrev,NRef]),
write('Already scored, updated refinement'),nl, write('Already scored, updated refinement'),nl,
write_rules([R3],user_output), write_rules([R3],user_output),
write('Score (CLL) '),write(Score),nl,nl,nl, write('Score '),write(Score),nl,nl,nl,
setting(beamsize,BS), setting(beamsize,BS),
insert_in_order(NB0,(R3,Score),BS,NB1), insert_in_order(NB0,(R3,Score),BS,NB1),
Nrev1 is Nrev+1, Nrev1 is Nrev+1,
@ -396,12 +396,11 @@ score_clause_refinements([R1|T],Nrev,NRef,DB,NB0,NB,CL0,CL,CLBG0,CLBG):-
), ),
format("Initial CLL ~f~n",[CLL0]), format("Initial CLL ~f~n",[CLL0]),
setting(random_restarts_REFnumber,N), setting(random_restarts_REFnumber,N),
random_restarts_ref(N,Nodes,CLL0,CLL,initial,Par,LE), random_restarts_ref(N,Nodes,CLL0,Score,initial,Par,LE),
end, end,
update_theory([R2],Par,[R3]), update_theory([R2],Par,[R3]),
write('Updated refinement'),nl, write('Updated refinement'),nl,
write_rules([R3],user_output), write_rules([R3],user_output),
Score = CLL,
write('Score (CLL) '),write(Score),nl,nl,nl, write('Score (CLL) '),write(Score),nl,nl,nl,
retract_all(Th1), retract_all(Th1),
retract_all([R2]),!, retract_all([R2]),!,