Sometimes, it is useful to build a model and save it for later use. This can be achieved with the java.io.Serializable interface that saves an object into a bytestream as a file. The opposite direction, that is, deserialization, restores the bytestream into the original object.
We will first build a sample model, that is, a J48 decision tree to demonstrate serialization and deserialization:
import java.io.BufferedReader;
import java.io.FileReader;
import weka.classifiers.trees.J48;
import weka.core.Instances;
...
J48 cls = new J48();
Instances inst = new Instances(new BufferedReader(new FileReader("dataset/titanic.arff")));
inst.setClassIndex(inst.numAttributes() - 1);
cls.buildClassifier(inst);
weka.core.SerializationHelper.write("j48.model", cls);
J48 cls2 = (J48) weka.core.SerializationHelper.read("j48.model");
System.out.println(cls2);First, we build a sample model, as shown in the Classification task.
Classifier cls = new J48();
Instances inst = new Instances(new BufferedReader(new FileReader("/dataset/titanic.arff")));
inst.setClassIndex(inst.numAttributes() - 1);
cls.buildClassifier(inst);Since classifiers have built-in support for serialization, simply call the write(String, Object) static method, which is located in the weka.core.SerializationHelper package with String indicating filename and Object as classifier:
weka.core.SerializationHelper.write("j48.model", cls);When you need to load the saved classifier, use the read (String) method located in the same package. Note, that the read() method loads the model as Object, so the model needs to be type-casted to an appropriate class, for example, J48 in our case:
J48 cls2 = (J48) weka.core.SerializationHelper.read("j48.model");Classifier is now loaded in the variable named cls2 and can be used as shown in other recipes.