yap-6.3/packages/ProbLog/problog_learning.yap

%%% -*- 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
%
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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 :-
	remove_queries,
	retractall(query_is_similar(_,_)),
	retractall(query_md5(_,_,_)).

remove_queries :-
	learning_flag(query_directory,Directory),
	user:example(ID,_,_),
	atomic_concat([Directory,'query_',ID],File),
	delete_file(File),
	fail.	
remove_queries.

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),
	    statistics(runtime,[_,T]),
	    my_format(3,' training example ~q: ~q after ~q msec~n',[ID,Query,T]),
%	    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).