Get risk predictions in WEKA using own Java code

java, machine-learning, prediction, probability, weka

Solution

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"

Problem

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• How to print out the predicted class after cross-validation in WEKA