Get risk predictions in WEKA using own Java code

I already checked the "Making predictions" documentation of WEKA and it contains explicit instructions for command line and GUI predictions.

I want to know how to get a prediction value like the one below I got from the GUI using the Agrawal dataset (weka.datagenerators.classifiers.classification.Agrawal) in my own Java code:

inst#,  actual,     predicted,  error,  prediction
1,      1:0,        2:1,        +,      0.941
2,      1:0,        1:0,        ,       1
3,      1:0,        1:0,        ,       1
4,      1:0,        1:0,        ,       1
5,      1:0,        1:0,        ,       1
6,      1:0,        1:0,        ,       1
7,      1:0,        2:1,        +,      0.941
8,      2:1,        2:1,        ,       0.941
9,      2:1,        2:1,        ,       0.941
10,     2:1,        2:1,        ,       0.941
1,      1:0,        1:0,        ,       1
2,      1:0,        1:0,        ,       1
3,      1:0,        1:0,        ,       1

I can't replicate this result even though it said that:

Java

If you want to perform the classification within your own code, see the classifying instances section of this article, explaining the Weka API in general.

I went to the link and it said:

Classifying instances

In case you have an unlabeled dataset that you want to classify with your newly trained classifier, you can use the following code snippet. It loads the file /some/where/unlabeled.arff, uses the previously built classifier tree to label the instances, and saves the labeled data as /some/where/labeled.arff.

This is not the case I want because I just want the k-fold cross validation predictions on my current dataset modeled.

Update

predictions

public FastVector predictions()

Returns the predictions that have been collected.

Returns:

a reference to the FastVector containing the predictions that have been collected. This should be null if no predictions have been collected.

I found the predictions() method for objects of type Evaluation and by using the code:

Object[] preds = evaluation.predictions().toArray();
for(Object pred : preds) {
    System.out.println(pred);
}

It resulted to:

...
NOM: 0.0 0.0 1.0 0.9466666666666667 0.05333333333333334
NOM: 0.0 0.0 1.0 0.8947368421052632 0.10526315789473684
NOM: 0.0 0.0 1.0 0.9934883720930232 0.0065116279069767444
NOM: 0.0 0.0 1.0 0.9466666666666667 0.05333333333333334
NOM: 0.0 0.0 1.0 0.9912575655682583 0.008742434431741762
NOM: 0.0 0.0 1.0 0.9934883720930232 0.0065116279069767444
...

Is this the same thing as the one above?

After deep Google searches (and because the documentation provides minimal help) I finally found the answer.

I hope this explicit answer helps others in the future.

For a sample code I saw the question "How to print out the predicted class after cross-validation in WEKA" and I'm glad I was able to decode the incomplete answer wherein some of it is hard to understand.

Here is my code that worked similar to the GUI's output
```
StringBuffer predictionSB = new StringBuffer();
Range attributesToShow = null;
Boolean outputDistributions = new Boolean(true);

PlainText predictionOutput = new PlainText();
predictionOutput.setBuffer(predictionSB);
predictionOutput.setOutputDistribution(true);

Evaluation evaluation = new Evaluation(data);
evaluation.crossValidateModel(j48Model, data, numberOfFolds,
        randomNumber, predictionOutput, attributesToShow,
        outputDistributions);
```
To help you understand, we need to implement the StringBuffer to be casted in an AbstractOutput object so that the function crossValidateModel can recognize it.

Using StringBuffer only will cause a java.lang.ClassCastException similar the one in the question while using a PlainText without a StringBuffer will show a java.lang.IllegalStateException.

I would like to thank ManChon U (Kevin) and their question "How to identify the cross-evaluation result to its corresponding instance in the input data set?" for giving me a clue on what this meant:

... you just need a single addition argument that is a concrete subclass of weka.classifiers.evaluation.output.prediction.AbstractOutput. weka.classifiers.evaluation.output.prediction.PlainText is probably the one you want to use. Source

and

... Try creating a PlainText object, which extends AbstractOutput (called output for example) instance and calling output.setBuffer(forPredictionsPrinting) and passing that in instead of the buffer. Source

These just actually meant to create a PlainText object, put a StringBuffer in it and use it to tweak the output with methods setOutput(boolean) and others.

Finally, to get our desired predictions, just use:
```
System.out.println(predictionOutput.getBuffer());
```
Wherein predictionOutput is an object from the AbstractOutput family (PlainText, CSV, XML, etc).
Additionally, the results of evaluation.predictions() is different from the one provided in the WEKA GUI. Fortunately Mark Hall explained this in the question "Print out the predict class after cross-validation"

Evaluation.predictions() returns a FastVector containing either NominalPrediction or NumericPrediction objects from the weka.classifiers.evaluation package. Calling Evaluation.crossValidateModel() with the additional AbstractOutput object results in the evaluation object printing the prediction/distribution information from Nominal/NumericPrediction objects to the StringBuffer in the format that you see in the Explorer or from the command line.

References:

"Print out the predict class after cross-validation"
"How to identify the cross-evaluation result to its corresponding instance in the input data set?"
"How to print out the predicted class after cross-validation in WEKA"