A second example that we consider is again a classification task, but this time with a kernelized version of the Passive Aggressive algorithm (KernelizedPassiveAggressiveClassification). The full code of this example can be found in the GitHub repository kelp-full, in particular in the source file HelloKernelLearning.java.
The dataset here used is again the svmlight page dataset, in the KeLP format.
As for the previous example, the dataset is composed by:
- Training set (2000 examples, 1000 of class “+1” (positive), and 1000 of class “-1” (negative))
- Test set (600 examples, 300 of class “+1” (positive), and 300 of class “-1” (negative))
Again, loading the dataset is simply a call to KeLP methods:
|
1 2 3 4 5 6 7 8 |
// Read a dataset into a trainingSet variable SimpleDataset trainingSet = new SimpleDataset(); trainingSet.populate("src/main/resources/hellolearning/train.klp"); // Read a dataset into a test variable SimpleDataset testSet = new SimpleDataset(); testSet.populate("src/main/resources/hellolearning/test.klp"); // define the positive class StringLabel positiveClass = new StringLabel("+1"); |
Using a kernel function within KeLP is very simple: it is sufficient to declare a kernel function, the representation on which it will operate and tell the algorithm the it must use a kernel function to compute similarity scores.
For example, if we want to use a Polynomial kernel on top of a linear kernel, it is sufficient to do as following:
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 |
// instantiate a passive aggressive algorithm KernelizedPassiveAggressive kPA = new KernelizedPassiveAggressive(); // indicate to the learner what is the positive class kPA.setLabel(positiveClass); // set an aggressiveness parameter kPA.setAggressiveness(0.01f); // use the first (and only here) representation Kernel linear = new LinearKernel("0"); // Normalize the linear kernel NormalizationKernel normalizedKernel = new NormalizationKernel(linear); // Apply a Polynomial kernel on the score (normalized) computed by the linear kernel Kernel polyKernel = new PolynomialKernel(2f, normalizedKernel); // tell the algorithm that the kernel we want to use in learning is the polynomial kernel kPA.setKernel(polyKernel); |
The rest of the Java code is very similar to the one of the Hello (linear) Learning example.