%%% -*- Mode: Prolog; -*-
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% $Date: 2009-06-17 22:22:00 +0200 (Mi, 17 Jun 2009) $
% $Revision: 1550 $
%
% This file is part of ProbLog
% http://dtai.cs.kuleuven.be/problog
%
% ProbLog was developed at Katholieke Universiteit Leuven
%
% Copyright 2009
% Angelika Kimmig, Vitor Santos Costa, Bernd Gutmann
%
% Main authors of this file:
% Bernd Gutmann
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Artistic License 2.0
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
:- module(learning,[do_learning/1,
do_learning/2,
set_learning_flag/2,
learning_flag/2,
learning_flags/0,
problog_help/0,
set_problog_flag/2,
problog_flag/2,
problog_flags/0,
auto_alpha/0
]).
% switch on all the checks to reduce bug searching time
:- style_check(all).
:- yap_flag(unknown,error).
% load modules from the YAP library
:- use_module(library(lists)).
:- use_module(library(random),[random/1]).
:- use_module(library(system),[file_exists/1,
file_property/2,
delete_file/1,
make_directory/1,
working_directory/2,
shell/1,
shell/2]).
% load our own modules
:- use_module('problog_learning/logger').
:- use_module('problog_learning/flags_learning').
:- use_module(problog).
% used to indicate the state of the system
:- dynamic values_correct/0.
:- dynamic learning_initialized/0.
:- dynamic current_iteration/1.
:- dynamic example_count/1.
:- dynamic query_probability_intern/2.
:- dynamic query_gradient_intern/3.
:- dynamic last_mse/1.
% used to identify queries which have identical proofs
:- dynamic query_is_similar/2.
:- dynamic query_md5/3.
%========================================================================
%=
%=
%=
%========================================================================
my_format(Level,String,Arguments) :-
learning_flag(verbosity_level,V_Level),
(
V_Level >= Level
->
(format(String,Arguments),flush_output(user));
true
).
%========================================================================
%= store the facts with the learned probabilities to a file
%= if F is a variable, a filename based on the current iteration is used
%=
%========================================================================
save_model:-
current_iteration(Iteration),
learning_flag(output_directory,Directory),
atomic_concat([Directory,'factprobs_',Iteration,'.pl'],F),
export_facts(F).
%========================================================================
%= store the current succes probabilities for training and test examples
%=
%========================================================================
save_predictions:-
current_iteration(Iteration),
learning_flag(output_directory,Directory),
atomic_concat([Directory,'predictions_',Iteration,'.pl'],F),
open(F,'append',Handle),
format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",[]),
format(Handle,"% Iteration, train/test, QueryID, Query, GroundTruth, Prediction %\n",[]),
format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",[]),
!,
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% start save prediction test examples
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
( % go over all test examples
current_predicate(user:test_example/3),
user:test_example(Query_ID,Query,TrueQueryProb),
query_probability(Query_ID,LearnedQueryProb),
format(Handle,'ex(~q,test,~q,~q,~10f,~10f).\n',
[Iteration,Query_ID,Query,TrueQueryProb,LearnedQueryProb]),
fail; % go to next test example
true
),
!,
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% stop save prediction test examples
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% start save prediction training examples
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
( % go over all training examples
current_predicate(user:example/3),
user:example(Query_ID,Query,TrueQueryProb),
query_probability(Query_ID,LearnedQueryProb),
format(Handle,'ex(~q,train,~q,~q,~10f,~10f).\n',
[Iteration,Query_ID,Query,TrueQueryProb,LearnedQueryProb]),
fail; % go to next training example
true
),
!,
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% stop save prediction training examples
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
format(Handle,'~3n',[]),
close(Handle).
%========================================================================
%= find out whether some example IDs are used more than once
%= if so, complain and stop
%=
%========================================================================
check_examples :-
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Check example IDs
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
(
(current_predicate(user:example/3),user:example(ID,_,_), \+ atomic(ID))
->
(
format(user_error,'The example id of training example ~q ',[ID]),
format(user_error,'is not atomic (e.g foo42, 23, bar, ...).~n',[]),
throw(error(examples))
); true
),
(
(current_predicate(user:test_example/3),user:test_example(ID,_,_), \+ atomic(ID))
->
(
format(user_error,'The example id of test example ~q ',[ID]),
format(user_error,'is not atomic (e.g foo42, 23, bar, ...).~n',[]),
throw(error(examples))
); true
),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Check example probabilities
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
(
(current_predicate(user:example/3),user:example(ID,_,P), (\+ number(P); P>1 ; P<0))
->
(
format(user_error,'The training example ~q does not have a valid probaility value (~q).~n',[ID,P]),
throw(error(examples))
); true
),
(
(current_predicate(user:test_example/3),user:test_example(ID,_,P), (\+ number(P); P>1 ; P<0))
->
(
format(user_error,'The test example ~q does not have a valid probaility value (~q).~n',[ID,P]),
throw(error(examples))
); true
),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Check that no example ID is repeated,
% and if it is repeated make sure the query is the same
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
(
(
(
current_predicate(user:example/3),
user:example(ID,QueryA,_),
user:example(ID,QueryB,_),
QueryA \= QueryB
) ;
(
current_predicate(user:test_example/3),
user:test_example(ID,QueryA,_),
user:test_example(ID,QueryB,_),
QueryA \= QueryB
);
(
current_predicate(user:example/3),
current_predicate(user:test_example/3),
user:example(ID,QueryA,_),
user:test_example(ID,QueryB,_),
QueryA \= QueryB
)
)
->
(
format(user_error,'The example id ~q is used several times.~n',[ID]),
throw(error(examples))
); true
).
%========================================================================
%= initialize everything and perform Iterations times gradient descent
%= can be called several times
%= if it is called with an epsilon parameter, it stops when the change
%= in the MSE is smaller than epsilon
%========================================================================
do_learning(Iterations) :-
do_learning(Iterations,-1).
do_learning(Iterations,Epsilon) :-
current_predicate(user:example/3),
!,
integer(Iterations),
number(Epsilon),
Iterations>0,
do_learning_intern(Iterations,Epsilon).
do_learning(_,_) :-
format(user_error,'~n~nWarning: No training examples specified !!!~n~n',[]).
do_learning_intern(0,_) :-
!.
do_learning_intern(Iterations,Epsilon) :-
Iterations>0,
init_learning,
current_iteration(CurrentIteration),
!,
retractall(current_iteration(_)),
!,
NextIteration is CurrentIteration+1,
assert(current_iteration(NextIteration)),
EndIteration is CurrentIteration+Iterations-1,
my_format(1,'~nIteration ~d of ~d~n',[CurrentIteration,EndIteration]),
logger_set_variable(iteration,CurrentIteration),
logger_start_timer(duration),
mse_testset,
once(ground_truth_difference),
gradient_descent,
learning_flag(log_frequency,Log_Frequency),
(
( Log_Frequency>0, 0 =:= CurrentIteration mod Log_Frequency)
->
(
once(save_predictions),
once(save_model)
);
true
),
update_values,
(
last_mse(Last_MSE)
->
(
retractall(last_mse(_)),
logger_get_variable(mse_trainingset,Current_MSE),
assert(last_mse(Current_MSE)),
!,
MSE_Diff is abs(Last_MSE-Current_MSE)
); (
logger_get_variable(mse_trainingset,Current_MSE),
assert(last_mse(Current_MSE)),
MSE_Diff is Epsilon+1
)
),
(
(learning_flag(rebuild_bdds,BDDFreq),BDDFreq>0,0 =:= CurrentIteration mod BDDFreq)
->
(
retractall(values_correct),
once(delete_all_queries),
once(init_queries)
); true
),
!,
logger_stop_timer(duration),
logger_write_data,
RemainingIterations is Iterations-1,
(
MSE_Diff>Epsilon
->
do_learning_intern(RemainingIterations,Epsilon);
true
).
%========================================================================
%= find proofs and build bdds for all training and test examples
%=
%=
%========================================================================
init_learning :-
learning_initialized,
!.
init_learning :-
check_examples,
logger_write_header,
my_format(1,'Initializing everything~n',[]),
empty_output_directory,
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Delete the BDDs from the previous run if they should
% not be reused
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
(
(
learning_flag(reuse_initialized_bdds,true),
learning_flag(rebuild_bdds,0)
)
->
true;
delete_all_queries
),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% start count test examples
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
bb_put(test_examples,0),
( % go over all test examples
current_predicate(user:test_example/3),
user:test_example(_,_,_),
bb_get(test_examples, OldCounter),
NewCounter is OldCounter+1,
bb_put(test_examples,NewCounter),
fail; % go to next text example
true
),
bb_delete(test_examples,TestExampleCount),
my_format(3,'~q test examples~n',[TestExampleCount]),
!,
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% stop count test examples
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% start count training examples
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
bb_put(training_examples,0),
( % go over all training examples
current_predicate(user:example/3),
user:example(_,_,_),
bb_get(training_examples, OldCounter),
NewCounter is OldCounter+1,
bb_put(training_examples,NewCounter),
fail; %go to next training example
true
),
bb_delete(training_examples,TrainingExampleCount),
assert(example_count(TrainingExampleCount)),
my_format(3,'~q training examples~n',[TrainingExampleCount]),
!,
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% stop count training examples
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% set learning rate and alpha
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
(
learning_flag(learning_rate,examples)
->
set_learning_flag(learning_rate,TrainingExampleCount);
true
),
(
learning_flag(alpha,auto)
->
auto_alpha;
true
),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% build BDD script for every example
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
once(initialize_fact_probabilities),
once(init_queries),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% done
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
assert(current_iteration(0)),
assert(learning_initialized),
my_format(1,'~n',[]).
%========================================================================
%=
%=
%=
%========================================================================
delete_all_queries :-
learning_flag(query_directory,Directory),
atomic_concat(['rm -f ',Directory,'query_*'],Command),
(shell(Command) -> true; true),
retractall(query_is_similar(_,_)),
retractall(query_md5(_,_,_)).
empty_output_directory :-
learning_flag(output_directory,Directory),
atomic_concat(['rm -f ',Directory,'log.dat ',
Directory,'factprobs_*.pl ',
Directory,'predictions_*.pl'],Command),
(shell(Command) -> true; true).
%========================================================================
%= This predicate goes over all training and test examples,
%= calls the inference method of ProbLog and stores the resulting
%= BDDs
%========================================================================
init_queries :-
my_format(2,'Build BDDs for examples~n',[]),
( % go over all test examples
current_predicate(user:test_example/3),
user:test_example(ID,Query,Prob),
my_format(3,' test example ~q: ~q~n',[ID,Query]),
flush_output(user),
init_one_query(ID,Query,test),
fail; % go to next test example
true
),
( % go over all training examples
current_predicate(user:example/3),
user:example(ID,Query,Prob),
my_format(3,' training example ~q: ~q~n',[ID,Query]),
flush_output(user),
init_one_query(ID,Query,training),
fail; %go to next training example
true
).
init_one_query(QueryID,Query,Type) :-
learning_flag(output_directory,Output_Directory),
learning_flag(query_directory,Query_Directory),
atomic_concat([Query_Directory,'query_',QueryID],Filename),
atomic_concat([Output_Directory,'input.txt'],Filename2),
atomic_concat([Output_Directory,'tmp_md5'],Filename3),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% if BDD file does not exist, call ProbLog
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
(
file_exists(Filename)
->
my_format(3,' Reuse existing BDD ~q~n~n',[Filename]);
(
learning_flag(init_method,(Query,_Prob,Filename,Filename2,InitCall)),
once(call(InitCall)),
delete_file(Filename2)
)
),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% check wether this BDD is similar to a previous one of the same type
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
(
learning_flag(check_duplicate_bdds,true)
->
(
% calculate the md5sum of the bdd script file
atomic_concat(['cat ',Filename,' | md5sum | sed "s/ .*$/\')./" | sed "s/^/md5(\'/"> ',Filename3],MD5Command),
(shell(MD5Command,0) -> true; throw(error("Something wrong with calculating the MD5 value"))),
open(Filename3, read, Handle),
read(Handle,md5(Query_MD5)),
close(Handle),
delete_file(Filename3),
% Does another query exists which already has this MD5?
(
query_md5(OtherQueryID,Query_MD5,Type)
->
% yippie! we can save a lot of work
(
assert(query_is_similar(QueryID,OtherQueryID)),
my_format(3, '~q is similar to ~q~2n', [QueryID,OtherQueryID])
); assert(query_md5(QueryID,Query_MD5,Type))
)
);
true
).
%========================================================================
%= set all unknown fact probabilities to random values
%=
%=
%========================================================================
initialize_fact_probabilities :-
( % go over all tunable facts
tunable_fact(FactID,_),
learning_flag(probability_initializer,(FactID,Probability,Query)),
once(call(Query)),
set_fact_probability(FactID,Probability),
fail; % go to next tunable fact
true
).
random_probability(_FactID,Probability) :-
% use probs around 0.5 to not confuse k-best search
random(Random),
Probability is 0.5+(Random-0.5)/100.
%========================================================================
%= updates all values of query_probability/2 and query_gradient/3
%= should be called always before these predicates are accessed
%= if the old values are still valid, nothing happens
%========================================================================
update_values :-
values_correct,
!.
update_values :-
\+ values_correct,
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% delete old values
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
once(retractall(query_probability_intern(_,_))),
once(retractall(query_gradient_intern(_,_,_))),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% start write current probabilities to file
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
learning_flag(output_directory,Directory),
atomic_concat(Directory,'input.txt',Input_Filename),
(
file_exists(Input_Filename)
->
delete_file(Input_Filename);
true
),
open(Input_Filename,'write',Handle),
( % go over all probabilistic fact
get_fact_probability(ID,Prob),
inv_sigmoid(Prob,Value),
(
non_ground_fact(ID)
->
format(Handle,'@x~q_*~n~10f~n',[ID,Value]);
format(Handle,'@x~q~n~10f~n',[ID,Value])
),
fail; % go to next probabilistic fact
true
),
close(Handle),
!,
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% stop write current probabilities to file
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
assert(values_correct).
%========================================================================
%=
%=
%=
%========================================================================
update_query_cleanup(QueryID) :-
(
(query_is_similar(QueryID,_) ; query_is_similar(_,QueryID))
->
% either this query is similar to another or vice versa,
% therefore we don't delete anything
true;
once(retractall(query_gradient_intern(QueryID,_,_)))
).
update_query(QueryID,Symbol,What_To_Update) :-
learning_flag(output_directory,Output_Directory),
learning_flag(query_directory,Query_Directory),
(
query_is_similar(QueryID,_)
->
% we don't have to evaluate the BDD
my_format(4,'#',[]);
(
learning_flag(sigmoid_slope,Slope),
problog_dir(PD),
((What_To_Update=all;query_is_similar(_,QueryID)) -> Method='g' ; Method='l'),
atomic_concat([PD,
'/ProblogBDD -i "',
Output_Directory,
'input.txt',
'" -l "',
Query_Directory,
'query_',
QueryID,
'" -m ',Method,' -id ',
QueryID,
' -sl ',Slope,
' > "',
Output_Directory,
'values.pl"'],Command),
shell(Command,Error),
(
Error = 2
->
throw(error('SimpleCUDD has been interrupted.'));
true
),
(
Error \= 0
->
throw(bdd_error(QueryID,Error));
true
),
atomic_concat([Output_Directory,'values.pl'],Values_Filename),
(
file_exists(Values_Filename)
->
(
(
once(my_load(Values_Filename,QueryID))
->
true;
(
format(user_error,'ERROR: Tried to read the file ~q but my_load/1 fails.~n~q.~2n',[Values_Filename,update_query(QueryID,Symbol,What_To_Update)]),
throw(error(my_load_fails))
)
);
(
format(user_error,'ERROR: Tried to read the file ~q but it does not exist.~n~q.~2n',[Values_Filename,update_query(QueryID,Symbol,What_To_Update)]),
throw(error(output_file_does_not_exist))
)
)
),
delete_file(Values_Filename),
my_format(4,'~w',[Symbol])
)
),
flush_output(user).
%========================================================================
%= This predicate reads probability and gradient values from the file
%= the gradient ID is a mere check to uncover hidden bugs
%= +Filename +QueryID -QueryProbability
%========================================================================
my_load(File,QueryID) :-
open(File,'read',Handle),
read(Handle,Atom),
once(my_load_intern(Atom,Handle,QueryID)),
!,
close(Handle).
my_load(File,QueryID) :-
format(user_error,'Error at ~q.~2n',[my_load(File,QueryID)]),
throw(error(my_load(File,QueryID))).
my_load_intern(end_of_file,_,_) :-
!.
my_load_intern(query_probability(QueryID,Prob),Handle,QueryID) :-
!,
assert(query_probability_intern(QueryID,Prob)),
read(Handle,X),
my_load_intern(X,Handle,QueryID).
my_load_intern(query_gradient(QueryID,XFactID,Value),Handle,QueryID) :-
!,
atomic_concat(x,StringFactID,XFactID),
atom_number(StringFactID,FactID),
assert(query_gradient_intern(QueryID,FactID,Value)),
read(Handle,X),
my_load_intern(X,Handle,QueryID).
my_load_intern(X,Handle,QueryID) :-
format(user_error,'Unknown atom ~q in results file.~n',[X]),
read(Handle,X),
my_load_intern(X,Handle,QueryID).
%========================================================================
%=
%=
%=
%========================================================================
query_probability(QueryID,Prob) :-
(
query_probability_intern(QueryID,Prob)
->
true;
(
query_is_similar(QueryID,OtherQueryID),
query_probability_intern(OtherQueryID,Prob)
)
).
query_gradient(QueryID,Fact,Value) :-
(
query_gradient_intern(QueryID,Fact,Value)
->
true;
(
query_is_similar(QueryID,OtherQueryID),
query_gradient_intern(OtherQueryID,Fact,Value)
)
).
%========================================================================
%=
%=
%=
%========================================================================
ground_truth_difference :-
findall(Diff,(tunable_fact(FactID,GroundTruth),
\+ var(GroundTruth),
get_fact_probability(FactID,Prob),
Diff is abs(GroundTruth-Prob)),AllDiffs),
% if no ground truth was specified for facts
% set everything to zero
(
AllDiffs=[]
->
(
MinDiff=0.0,
MaxDiff=0.0,
DiffMean=0.0
) ; (
length(AllDiffs,Len),
sum_list(AllDiffs,AllDiffsSum),
min_list(AllDiffs,MinDiff),
max_list(AllDiffs,MaxDiff),
DiffMean is AllDiffsSum/Len
)
),
logger_set_variable(ground_truth_diff,DiffMean),
logger_set_variable(ground_truth_mindiff,MinDiff),
logger_set_variable(ground_truth_maxdiff,MaxDiff).
%========================================================================
%= Calculates the mse of training and test data
%=
%= -Float
%========================================================================
mse_trainingset_only_for_linesearch(MSE) :-
(
current_predicate(user:example/3)
->
(
update_values,
findall(SquaredError,
(user:example(QueryID,_Query,QueryProb),
once(update_query(QueryID,'.',probability)),
query_probability(QueryID,CurrentProb),
once(update_query_cleanup(QueryID)),
SquaredError is (CurrentProb-QueryProb)**2),
AllSquaredErrors),
length(AllSquaredErrors,Length),
sum_list(AllSquaredErrors,SumAllSquaredErrors),
MSE is SumAllSquaredErrors/Length,
my_format(3,' (~8f)~n',[MSE])
); true
),
retractall(values_correct).
% calculate the mse of the test data
mse_testset :-
(
current_predicate(user:test_example/3)
->
(
my_format(2,'MSE_Test ',[]),
update_values,
findall(SquaredError,
(user:test_example(QueryID,_Query,QueryProb),
once(update_query(QueryID,'+',probability)),
query_probability(QueryID,CurrentProb),
once(update_query_cleanup(QueryID)),
SquaredError is (CurrentProb-QueryProb)**2),
AllSquaredErrors),
length(AllSquaredErrors,Length),
sum_list(AllSquaredErrors,SumAllSquaredErrors),
min_list(AllSquaredErrors,MinError),
max_list(AllSquaredErrors,MaxError),
MSE is SumAllSquaredErrors/Length,
logger_set_variable(mse_testset,MSE),
logger_set_variable(mse_min_testset,MinError),
logger_set_variable(mse_max_testset,MaxError),
my_format(2,' (~8f)~n',[MSE])
); true
).
%========================================================================
%= Calculates the sigmoid function respectivly the inverse of it
%= warning: applying inv_sigmoid to 0.0 or 1.0 will yield +/-inf
%=
%= +Float, -Float
%========================================================================
sigmoid(T,Sig) :-
learning_flag(sigmoid_slope,Slope),
Sig is 1/(1+exp(-T*Slope)).
inv_sigmoid(T,InvSig) :-
learning_flag(sigmoid_slope,Slope),
InvSig is -log(1/T-1)/Slope.
%========================================================================
%= Perform one iteration of gradient descent
%=
%= assumes that everything is initialized, if the current values
%= of query_probability/2 and query_gradient/3 are not up to date
%= they will be recalculated
%= finally, the values_correct/0 is retracted to signal that the
%= probabilities of the examples have to be recalculated
%========================================================================
save_old_probabilities :-
( % go over all tunable facts
tunable_fact(FactID,_),
get_fact_probability(FactID,OldProbability),
atomic_concat(['old_prob_',FactID],Key),
bb_put(Key,OldProbability),
fail; % go to next tunable fact
true
).
forget_old_probabilities :-
( % go over all tunable facts
tunable_fact(FactID,_),
atomic_concat(['old_prob_',FactID],Key),
atomic_concat(['grad_',FactID],Key2),
bb_delete(Key,_),
bb_delete(Key2,_),
fail; % go to next tunable fact
true
).
add_gradient(Learning_Rate) :-
( % go over all tunable facts
tunable_fact(FactID,_),
atomic_concat(['old_prob_',FactID],Key),
atomic_concat(['grad_',FactID],Key2),
bb_get(Key,OldProbability),
bb_get(Key2,GradValue),
inv_sigmoid(OldProbability,OldValue),
NewValue is OldValue -Learning_Rate*GradValue,
sigmoid(NewValue,NewProbability),
% Prevent "inf" by using values too close to 1.0
Prob_Secure is min(0.999999999,max(0.000000001,NewProbability)),
set_fact_probability(FactID,Prob_Secure),
fail; % go to next tunable fact
true
),
!,
retractall(values_correct).
gradient_descent :-
my_format(2,'Gradient ',[]),
save_old_probabilities,
update_values,
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% start set gradient to zero
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
( % go over all tunable facts
tunable_fact(FactID,_),
atomic_concat(['grad_',FactID],Key),
bb_put(Key,0.0),
fail; % go to next tunable fact
true
),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 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),
learning_flag(alpha,Alpha),
logger_set_variable(alpha,Alpha),
example_count(Example_Count),
( % go over all training examples
current_predicate(user:example/3),
user:example(QueryID,_Query,QueryProb),
once(update_query(QueryID,'.',all)),
query_probability(QueryID,BDDProb),
(
QueryProb=:=0.0
->
Y2=Alpha;
Y2=1.0
),
Y is Y2*2/Example_Count * (BDDProb-QueryProb),
% first do the calculations for the MSE on training set
Squared_Error is (BDDProb-QueryProb)**2,
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),
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),
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),
( % go over all tunable facts
tunable_fact(FactID,_),
atomic_concat(['grad_',FactID],Key),
% if the following query fails,
% it means, the fact is not used in the proof
% of QueryID, and the gradient is 0.0 and will
% not contribute to NewValue either way
% DON'T FORGET THIS IF YOU CHANGE SOMETHING HERE!
query_gradient(QueryID,FactID,GradValue),
bb_get(Key,OldValue),
NewValue is OldValue + Y*GradValue,
bb_put(Key,NewValue),
fail; % go to next fact
true
),
once(update_query_cleanup(QueryID)),
fail; % go to next training example
true
),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% stop calculate gradient
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
!,
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% start statistics on gradient
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
findall(V, (
tunable_fact(FactID,_),
atomic_concat(['grad_',FactID],Key),
bb_get(Key,V)
),Gradient_Values),
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),
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),
my_format(2,'~n',[]),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% start add gradient to current probabilities
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
(
learning_flag(line_search,false)
->
learning_flag(learning_rate,LearningRate);
lineSearch(LearningRate,_)
),
my_format(3,'learning rate:~8f~n',[LearningRate]),
add_gradient(LearningRate),
logger_set_variable(learning_rate,LearningRate),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% stop add gradient to current probabilities
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
!,
forget_old_probabilities.
%========================================================================
%=
%=
%========================================================================
line_search_evaluate_point(Learning_Rate,MSE) :-
add_gradient(Learning_Rate),
my_format(2,'Line search (h=~8f) ',[Learning_Rate]),
mse_trainingset_only_for_linesearch(MSE).
lineSearch(Final_X,Final_Value) :-
% Get Parameters for line search
learning_flag(line_search_tolerance,Tol),
learning_flag(line_search_tau,Tau),
learning_flag(line_search_interval,(A,B)),
my_format(3,'Line search in interval (~4f,~4f)~n',[A,B]),
% init values
Acc is Tol * (B-A),
InitRight is A + Tau*(B-A),
InitLeft is A + B - InitRight,
line_search_evaluate_point(A,Value_A),
line_search_evaluate_point(B,Value_B),
line_search_evaluate_point(InitRight,Value_InitRight),
line_search_evaluate_point(InitLeft,Value_InitLeft),
bb_put(line_search_a,A),
bb_put(line_search_b,B),
bb_put(line_search_left,InitLeft),
bb_put(line_search_right,InitRight),
bb_put(line_search_value_a,Value_A),
bb_put(line_search_value_b,Value_B),
bb_put(line_search_value_left,Value_InitLeft),
bb_put(line_search_value_right,Value_InitRight),
bb_put(line_search_iteration,1),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% BEGIN BACK TRACKING
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
(
repeat,
bb_get(line_search_iteration,Iteration),
bb_get(line_search_a,Ak),
bb_get(line_search_b,Bk),
bb_get(line_search_left,Left),
bb_get(line_search_right,Right),
bb_get(line_search_value_a,Fl),
bb_get(line_search_value_b,Fr),
bb_get(line_search_value_left,FLeft),
bb_get(line_search_value_right,FRight),
(
% check for infinity, if there is, go to the left
( FLeft >= FRight, \+ FLeft = (+inf), \+ FRight = (+inf) )
->
(
AkNew=Left,
FlNew=FLeft,
LeftNew=Right,
FLeftNew=FRight,
RightNew is AkNew + Bk - LeftNew,
line_search_evaluate_point(RightNew,FRightNew),
BkNew=Bk,
FrNew=Fr
);
(
BkNew=Right,
FrNew=FRight,
RightNew=Left,
FRightNew=FLeft,
LeftNew is Ak + BkNew - RightNew,
line_search_evaluate_point(LeftNew,FLeftNew),
AkNew=Ak,
FlNew=Fl
)
),
Next_Iteration is Iteration + 1,
bb_put(line_search_iteration,Next_Iteration),
bb_put(line_search_a,AkNew),
bb_put(line_search_b,BkNew),
bb_put(line_search_left,LeftNew),
bb_put(line_search_right,RightNew),
bb_put(line_search_value_a,FlNew),
bb_put(line_search_value_b,FrNew),
bb_put(line_search_value_left,FLeftNew),
bb_put(line_search_value_right,FRightNew),
% is the search interval smaller than the tolerance level?
BkNew-AkNew<Acc,
% apperantly it is, so get me out of here and
% cut away the choice point from repeat
!
),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% END BACK TRACKING
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% clean up the blackboard mess
bb_delete(line_search_iteration,_),
bb_delete(line_search_a,_),
bb_delete(line_search_b,_),
bb_delete(line_search_left,_),
bb_delete(line_search_right,_),
bb_delete(line_search_value_a,_),
bb_delete(line_search_value_b,_),
bb_delete(line_search_value_left,_),
bb_delete(line_search_value_right,_),
% it doesn't harm to check also the value in the middle
% of the current search interval
Middle is (AkNew + BkNew) / 2.0,
line_search_evaluate_point(Middle,Value_Middle),
% return the optimal value
my_5_min(Value_Middle,FlNew,FrNew,FLeftNew,FRightNew,
Middle,AkNew,BkNew,LeftNew,RightNew,
Optimal_Value,Optimal_X),
line_search_postcheck(Optimal_Value,Optimal_X,Final_Value,Final_X).
line_search_postcheck(V,X,V,X) :-
X>0,
!.
line_search_postcheck(V,X,V,X) :-
learning_flag(line_search_never_stop,false),
!.
line_search_postcheck(_,_, LLH, FinalPosition) :-
learning_flag(line_search_tolerance,Tolerance),
learning_flag(line_search_interval,(Left,Right)),
Offset is (Right - Left) * Tolerance,
bb_put(line_search_offset,Offset),
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
(
repeat,
bb_get(line_search_offset,OldOffset),
NewOffset is OldOffset * Tolerance,
bb_put(line_search_offset,NewOffset),
Position is Left + NewOffset,
set_linesearch_weights_calc_llh(Position,LLH),
bb_put(line_search_llh,LLH),
write(logAtom(lineSearchPostCheck(Position,LLH))),nl,
\+ LLH = (+inf),
!
), % cut away choice point from repeat
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
bb_delete(line_search_llh,LLH),
bb_delete(line_search_offset,FinalOffset),
FinalPosition is Left + FinalOffset.
my_5_min(V1,V2,V3,V4,V5,F1,F2,F3,F4,F5,VMin,FMin) :-
(
V1<V2
->
(VTemp1=V1,FTemp1=F1);
(VTemp1=V2,FTemp1=F2)
),
(
V3<V4
->
(VTemp2=V3,FTemp2=F3);
(VTemp2=V4,FTemp2=F4)
),
(
VTemp1<VTemp2
->
(VTemp3=VTemp1,FTemp3=FTemp1);
(VTemp3=VTemp2,FTemp3=FTemp2)
),
(
VTemp3<V5
->
(VMin=VTemp3,FMin=FTemp3);
(VMin=V5,FMin=F5)
).
%========================================================================
%= set the alpha parameter to the value
%= # positive training examples / # negative training examples
%=
%= training example is positive if P(e)=1
%= training example is negative if P(e)=0
%=
%= if there are training example with 0<P<1, set alpha=1.0
%========================================================================
auto_alpha :-
\+ current_predicate(user:example/3),
set_learning_flag(alpha,1.0).
auto_alpha :-
user:example(_,_,P),
P<1,
P>0,
!,
set_learning_flag(alpha,1.0).
auto_alpha :-
findall(1,(user:example(_,_,P),P=:=1.0),Pos),
findall(0,(user:example(_,_,P),P=:=0.0),Neg),
length(Pos,NP),
length(Neg,NN),
Alpha is NP/NN,
set_learning_flag(alpha,Alpha).
%========================================================================
%= initialize the logger module and set the flags for learning
%= don't change anything here! use set_learning_flag/2 instead
%========================================================================
global_initialize :-
set_learning_flag(output_directory,'./output'),
set_learning_flag(query_directory,'./queries'),
set_learning_flag(log_frequency,1),
set_learning_flag(rebuild_bdds,0),
set_learning_flag(reuse_initialized_bdds,false),
set_learning_flag(learning_rate,examples),
set_learning_flag(check_duplicate_bdds,true),
set_learning_flag(probability_initializer,(FactID,P,random_probability(FactID,P))),
set_learning_flag(alpha,auto),
set_learning_flag(sigmoid_slope,1.0), % 1.0 gives standard sigmoid
set_learning_flag(init_method,(Query,Probability,BDDFile,ProbFile,
problog_kbest_save(Query,100,Probability,_Status,BDDFile,ProbFile))),
set_learning_flag(line_search,false),
set_learning_flag(line_search_never_stop,true),
set_learning_flag(line_search_tau,0.618033988749),
set_learning_flag(line_search_tolerance,0.05),
set_learning_flag(line_search_interval,(0,100)),
set_learning_flag(verbosity_level,5),
logger_define_variable(iteration, int),
logger_define_variable(duration,time),
logger_define_variable(mse_trainingset,float),
logger_define_variable(mse_min_trainingset,float),
logger_define_variable(mse_max_trainingset,float),
logger_define_variable(mse_testset,float),
logger_define_variable(mse_min_testset,float),
logger_define_variable(mse_max_testset,float),
logger_define_variable(gradient_mean,float),
logger_define_variable(gradient_min,float),
logger_define_variable(gradient_max,float),
logger_define_variable(ground_truth_diff,float),
logger_define_variable(ground_truth_mindiff,float),
logger_define_variable(ground_truth_maxdiff,float),
logger_define_variable(learning_rate,float),
logger_define_variable(alpha,float).
%========================================================================
%=
%=
%========================================================================
:- initialization(global_initialize).