speedup.
This commit is contained in:
Vitor Santos Costa
parent
7458d8ee74
commit
15bcaf97fc
@ -620,6 +620,7 @@ init_one_query(QueryID,Query,Type) :-
|
||||
rb_new(H0),
|
||||
maplist_to_hash(MapList, H0, Hash),
|
||||
tree_to_grad(Tree, Hash, [], Grad),
|
||||
%% %writeln(Call:Tree),
|
||||
recordz(QueryID,bdd(Grad,MapList),_)
|
||||
)
|
||||
),
|
||||
@ -712,15 +713,10 @@ gradient(QueryID, l, Slope) :-
|
||||
fail.
|
||||
gradient(_QueryID, l, _).
|
||||
gradient(QueryID, g, Slope) :-
|
||||
/*
|
||||
query_gradient(17,x2,p,6.736196e-02).
|
||||
query_probability(17,1.173512e-01).
|
||||
*/
|
||||
recorded(QueryID, bdd(Tree, MapList), _),
|
||||
bind_maplist(MapList),
|
||||
member(I-_, MapList),
|
||||
run_grad(Tree, I, Slope, 0.0, Grad),
|
||||
% writeln(query_gradient_intern(QueryID,I,p,Grad)),
|
||||
assert(query_gradient_intern(QueryID,I,p,Grad)),
|
||||
fail.
|
||||
gradient(QueryID, g, Slope) :-
|
||||
@ -827,6 +823,8 @@ query_probability(QueryID,Prob) :-
|
||||
query_probability_intern(OtherQueryID,Prob)
|
||||
)
|
||||
).
|
||||
query_gradient(QueryID,Fact,p,Value) :- !,
|
||||
query_gradient_intern(QueryID,Fact,p,Value).
|
||||
query_gradient(QueryID,Fact,Type,Value) :-
|
||||
(
|
||||
query_gradient_intern(QueryID,Fact,Type,Value)
|
||||
@ -1079,7 +1077,9 @@ add_gradient(Learning_Rate) :-
|
||||
retractall(values_correct).
|
||||
|
||||
|
||||
% vsc: avoid silly search
|
||||
gradient_descent :-
|
||||
continuous_fact(_), !,
|
||||
current_iteration(Iteration),
|
||||
create_training_predictions_file_name(Iteration,File_Name),
|
||||
open(File_Name,'write',Handle),
|
||||
@ -1288,6 +1288,173 @@ gradient_descent :-
|
||||
!,
|
||||
forget_old_probabilities.
|
||||
|
||||
% VSC: no continuous facts
|
||||
% simplify code
|
||||
gradient_descent :-
|
||||
current_iteration(Iteration),
|
||||
create_training_predictions_file_name(Iteration,File_Name),
|
||||
open(File_Name,'write',Handle),
|
||||
format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%~n",[]),
|
||||
format(Handle,"% Iteration, train/test, QueryID, Query, GroundTruth, Prediction %~n",[]),
|
||||
format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%~n",[]),
|
||||
|
||||
format_learning(2,'Gradient ',[]),
|
||||
|
||||
save_old_probabilities,
|
||||
update_values,
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
% start set gradient to zero
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
forall(tunable_fact(FactID,_),
|
||||
(
|
||||
(
|
||||
atomic_concat(['grad_',FactID],Key),
|
||||
bb_put(Key,0.0)
|
||||
)
|
||||
)
|
||||
),
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
% stop gradient to zero
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
% start calculate gradient
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
bb_put(mse_train_sum, 0.0),
|
||||
bb_put(mse_train_min, 0.0),
|
||||
bb_put(mse_train_max, 0.0),
|
||||
bb_put(llh_training_queries, 0.0),
|
||||
|
||||
problog_flag(alpha,Alpha),
|
||||
logger_set_variable(alpha,Alpha),
|
||||
example_count(Example_Count),
|
||||
|
||||
forall(user:example(QueryID,Query,QueryProb,Type),
|
||||
(
|
||||
once(update_query(QueryID,'.',all)),
|
||||
query_probability(QueryID,BDDProb),
|
||||
format(Handle,'ex(~q,train,~q,~q,~10f,~10f).~n',[Iteration,QueryID,Query,QueryProb,BDDProb]),
|
||||
(
|
||||
QueryProb=:=0.0
|
||||
->
|
||||
Y2=Alpha;
|
||||
Y2=1.0
|
||||
),
|
||||
(
|
||||
(Type == '='; (Type == '<', BDDProb>QueryProb); (Type=='>',BDDProb<QueryProb))
|
||||
->
|
||||
Y is Y2*2/Example_Count * (BDDProb-QueryProb);
|
||||
Y=0.0
|
||||
),
|
||||
|
||||
|
||||
% first do the calculations for the MSE on training set
|
||||
(
|
||||
(Type == '='; (Type == '<', BDDProb>QueryProb); (Type=='>',BDDProb<QueryProb))
|
||||
->
|
||||
Squared_Error is (BDDProb-QueryProb)**2;
|
||||
Squared_Error=0.0
|
||||
),
|
||||
|
||||
bb_get(mse_train_sum,Old_MSE_Train_Sum),
|
||||
bb_get(mse_train_min,Old_MSE_Train_Min),
|
||||
bb_get(mse_train_max,Old_MSE_Train_Max),
|
||||
bb_get(llh_training_queries,Old_LLH_Training_Queries),
|
||||
New_MSE_Train_Sum is Old_MSE_Train_Sum+Squared_Error,
|
||||
New_MSE_Train_Min is min(Old_MSE_Train_Min,Squared_Error),
|
||||
New_MSE_Train_Max is max(Old_MSE_Train_Max,Squared_Error),
|
||||
New_LLH_Training_Queries is Old_LLH_Training_Queries+log(BDDProb),
|
||||
bb_put(mse_train_sum,New_MSE_Train_Sum),
|
||||
bb_put(mse_train_min,New_MSE_Train_Min),
|
||||
bb_put(mse_train_max,New_MSE_Train_Max),
|
||||
bb_put(llh_training_queries,New_LLH_Training_Queries),
|
||||
|
||||
|
||||
|
||||
( % go over all tunable facts
|
||||
query_gradient(QueryID,FactID,p,GradValue),
|
||||
atomic_concat(['grad_',FactID],Key),
|
||||
bb_get(Key,OldValue),
|
||||
NewValue is OldValue + Y*GradValue,
|
||||
bb_put(Key,NewValue),
|
||||
fail; % go to next fact
|
||||
true
|
||||
),
|
||||
|
||||
once(update_query_cleanup(QueryID))
|
||||
)),
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
% stop calculate gradient
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
!,
|
||||
|
||||
close(Handle),
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
% start statistics on gradient
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
findall(V, (
|
||||
tunable_fact(FactID,_),
|
||||
atomic_concat(['grad_',FactID],Key),
|
||||
bb_get(Key,V)
|
||||
),Gradient_Values),
|
||||
|
||||
(
|
||||
Gradient_Values==[]
|
||||
->
|
||||
(
|
||||
logger_set_variable(gradient_mean,0.0),
|
||||
logger_set_variable(gradient_min,0.0),
|
||||
logger_set_variable(gradient_max,0.0)
|
||||
);
|
||||
(
|
||||
sum_list(Gradient_Values,GradSum),
|
||||
max_list(Gradient_Values,GradMax),
|
||||
min_list(Gradient_Values,GradMin),
|
||||
length(Gradient_Values,GradLength),
|
||||
GradMean is GradSum/GradLength,
|
||||
|
||||
logger_set_variable(gradient_mean,GradMean),
|
||||
logger_set_variable(gradient_min,GradMin),
|
||||
logger_set_variable(gradient_max,GradMax)
|
||||
)
|
||||
),
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
% stop statistics on gradient
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
|
||||
bb_delete(mse_train_sum,MSE_Train_Sum),
|
||||
bb_delete(mse_train_min,MSE_Train_Min),
|
||||
bb_delete(mse_train_max,MSE_Train_Max),
|
||||
bb_delete(llh_training_queries,LLH_Training_Queries),
|
||||
MSE is MSE_Train_Sum/Example_Count,
|
||||
|
||||
logger_set_variable(mse_trainingset,MSE),
|
||||
logger_set_variable(mse_min_trainingset,MSE_Train_Min),
|
||||
logger_set_variable(mse_max_trainingset,MSE_Train_Max),
|
||||
logger_set_variable(llh_training_queries,LLH_Training_Queries),
|
||||
|
||||
format_learning(2,'~n',[]),
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
% start add gradient to current probabilities
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
(
|
||||
problog_flag(line_search,false)
|
||||
->
|
||||
problog_flag(learning_rate,LearningRate);
|
||||
lineSearch(LearningRate,_)
|
||||
),
|
||||
format_learning(3,'learning rate:~8f~n',[LearningRate]),
|
||||
add_gradient(LearningRate),
|
||||
logger_set_variable(learning_rate,LearningRate),
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
% stop add gradient to current probabilities
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
!,
|
||||
forget_old_probabilities.
|
||||
|
||||
%========================================================================
|
||||
%=
|
||||
%=
|
||||
|
||||
Reference in New Issue
Block a user