Evaluation

Evaluation metrics are encaspulated in the Evaluator interface:

public interface Evaluator<T> {

	/**
	 * Sets the classifier to use for evaluation
	 * @param cls the classifier
	 */
	public void setClassifier(Classifier<T> cls);
	
	/**
	 * Sets the list of training samples on which to train the classifier
	 * @param trainlist the training set
	 */
	public void setTrainingSet(List<TrainingSample<T>> trainlist);
	
	/**
	 * Sets the list of testing samples on which to evaluate the classifier
	 * @param testlist the testing set
	 */
	public void setTestingSet(List<TrainingSample<T>> testlist);
	
	/**
	 * Run the training procedure and compute score.
	 */
	public void evaluate();
	
	/**
	 * Tells the score resulting of the evaluation
	 * @return the score
	 */
	public double getScore();
	
}

Currently, accuracy and average precision are available as metrics. They are agnostic regarding data type.

To use The Evaluator interface, you need a Classifier<T> c (not trained), a training List<TrainingSample<T>> train and a testing List<TrainingSample<T>> test. You should set these using the setters methods of the Evaluator:

	eval.setClassifier(c);
	eval.setTrainingSet(train);
	eval.setTestingSet(test);

Evaluation is performed calling the evaluate() method. The training is ensured by the Evaluator itself:

	eval.evaluate();

Scores can be obtained calling the getScore() method:

	System.out.println("Accuracy: " + eval.getScore());