How to set parameter of SMO?

IngoRMIngoRM Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, Community Manager, RMResearcher, Member, University Professor Posts: 1,751 RM Founder
edited November 2018 in Help
Original message from SourceForge forum at http://sourceforge.net/forum/forum.php?thread_id=2038567&;forum_id=390413

I want to use SMO to get well-calibrated probability
I call the "setparameter" method in my program,but the output's probability is still equal to 0 or 1, i.e. it doesn't work.(I call the method getParameter("M"), the result is "true"! , not default value "false")
But I run SMO by GUI, I can set parameter to get well-calibrated probability

I also try to set others parameters of SMO or other Weka algorithm. it is also invalid. But I can set parameter in non-weka algorithm, it's valid

Someone please help me...

My Java Code: 
//build model
Operator SMOOperator = OperatorService.createOperator("W-SMO"); 
SMOOperator.setParameter("M", "true");
Learner learner = (Learner) SMOOperator;
Model model = learner.learn(trainingset);

// apply model
IOContainer testingSetContainer = testingSetContainer.append(model);
Operator modelApp = OperatorService.createOperator(ModelApplier.class);
testingSetContainer = modelApp.apply(testingSetContainer);

// print results
ExampleSet resultSet = resultSetContainer.get(ExampleSet.class); 
ExampleTable tableReader = resultSet.getExampleTable();
DataRowReader rowReader = tableReader.getDataRowReader();
DataRow row; 

Attribute aid = resultSet.getAttributes().getId();
Attribute apl = resultSet.getAttributes().getPredictedLabel();
Attribute al = resultSet.getAttributes().getLabel();
Attribute ac = resultSet.getAttributes().get("confidence(1)");

System.out.println("id = "+row.get(aid)+" label = "+row.get(al)+" prediction = "+row.get(apl)+" confidence = "+row.get(ac));

id = 685.0 label = 0.0 prediction = 0.0 confidence = 0.0
id = 701.0 label = 0.0 prediction = 0.0 confidence = 0.0
id = 697.0 label = 0.0 prediction = 0.0 confidence = 0.0

Answer by Harri:

In SMO, you have to set the logistic parameter (I forget exact name but there is only one) 'true' to get true probabilities. 


Answer by topic starter:

I have been checked the tutorial. 
In SMO, the description of parameter "M" is 

M: Fit logistic models to SVM outputs. (boolean; default: false)

I am sure that I set the "correct" parameter, correct parameter name and correct value
(if I type wrong name ex. "m"(lowercase) or wrong value ex. "trrue" then the error information will print out)

Why I set "M" parameter in RapidMiner GUI is work, but in java code is wrong?

I also try to set other parameter value randomly, such as 
C: The complexity constant C. (default 1) (real; -1-+1) 
L: The tolerance parameter. (default 1.0e-3) (real; -1-+1)

My accuracy is not change. it seems that I can only use default setting
This problem also exist in Weka learning algorithm "W-MultilayerPerceptron", 
but not exist in RapidMiner learning algorithm "DecisionTree" 

this problem is very confused.

Answer by Ingo Mierswa:


does it also apply if you use

Model model = SMOOperator.apply(new IOContainer(trainingset)).getInput(Model.class);

instead of 

Learner learner = (Learner) SMOOperator; 
Model model = learner.learn(trainingset); 

Probably the parameters are not given to the Weka operators in the learn(...) method.


Answer by topic starter:


the line of code will occur compile error...

Model model = SMOOperator.apply(new IOContainer(trainingset)).getInput(Model.class);

In IOContainer class, getInput() method is not exist
I try to revise these code like this...

Operator SMOOperator = OperatorService.createOperator("W-SMO"); 
SMOOperator.setParameter("M", "true"); 
try {
Model model = SMOOperator.apply(trainingSetIOContainer).get(Model.class);
} catch (OperatorException e1) {

But the parameter value which I set is also invalid, the problem still exist in Weka learning algorithm

Do someone know other way to set parameter value for weka operator?

Answer by Ingo Mierswa:

Ok, I found the reason: since all Weka operators are dynamically created the parameters are not initialized directly after operator creation and you have to do this manually. Just use the following lines

Operator smoOperator = OperatorService.createOperator("W-SMO");
smoOperator.getParameterTypes(); // <-- this is the important workaround
smoOperator.setParameter("M", "true");

for operator creation and parameter setting and it will work. I will also add a fix to the CVS version of RapidMiner so that you will not have to invoke "getParameterTypes()" for the Weka operators for future versions of RapidMiner.

Hope that helps. Cheers,
Sign In or Register to comment.