public static void main(String[] args) throws Exception {
/*
* First we load the test data from our ARFF file
*/
ArffLoader testLoader = new ArffLoader();
testLoader.setSource(new File("data/titanic/test.arff"));
testLoader.setRetrieval(Loader.BATCH);
Instances testDataSet = testLoader.getDataSet();
/*
* Now we tell the data set which attribute we want to classify, in our
* case, we want to classify the first column: survived
*/
Attribute testAttribute = testDataSet.attribute(0);
testDataSet.setClass(testAttribute);
testDataSet.deleteStringAttributes();
/*
* Now we read in the serialized model from disk
*/
Classifier classifier = (Classifier) SerializationHelper.read("data/titanic/titanic.model");
/*
* This part may be a little confusing. We load up the test data again
* so we have a prediction data set to populate. As we iterate over the
* first data set we also iterate over the second data set. After an
* instance is classified, we set the value of the prediction data set
* to be the value of the classification
*/
ArffLoader test1Loader = new ArffLoader();
test1Loader.setSource(new File("data/titanic/test.arff"));
Instances test1DataSet = test1Loader.getDataSet();
Attribute test1Attribute = test1DataSet.attribute(0);
test1DataSet.setClass(test1Attribute);
/*
* Now we iterate over the test data and classify each entry and set the
* value of the 'survived' column to the result of the classification
*/
Enumeration testInstances = testDataSet.enumerateInstances();
Enumeration test1Instances = test1DataSet.enumerateInstances();
while (testInstances.hasMoreElements()) {
Instance instance = (Instance) testInstances.nextElement();
Instance instance1 = (Instance) test1Instances.nextElement();
double classification = classifier.classifyInstance(instance);
instance1.setClassValue(classification);
}
/*
* Now we want to write out our predictions. The resulting file is in a
* format suitable to submit to Kaggle.
*/
CSVSaver predictedCsvSaver = new CSVSaver();
predictedCsvSaver.setFile(new File("data/titanic/predict.csv"));
predictedCsvSaver.setInstances(test1DataSet);
predictedCsvSaver.writeBatch();
System.out.println("Prediciton saved to predict.csv");
}
public static void writeFile(Instances dataSet, String path, String fileName, String extension) {
/*
path : "dataSet\\"
filename : "accelerometer_instances"
extension : "csv"
*/
try {
if (extension.equals("arff")) {
ArffSaver arffSaver = new ArffSaver();
arffSaver.setInstances(dataSet);
arffSaver.setFile(new File(path + fileName + "." + extension));
arffSaver.writeBatch();
} else if (extension.equals("csv")) {
CSVSaver csvSaver = new CSVSaver();
csvSaver.setInstances(dataSet);
csvSaver.setFile(new File(path + fileName + "." + extension));
csvSaver.writeBatch();
} else {
System.out.println("arff or csv only!");
System.exit(-1);
}
} catch (IOException e) {
e.printStackTrace();
}
}
public static void main(String[] args) throws Exception {
// NaiveBayesSimple nb = new NaiveBayesSimple();
// BufferedReader br_train = new BufferedReader(new FileReader("src/train.arff.txt"));
// String s = null;
// long st_time = System.currentTimeMillis();
// Instances inst_train = new Instances(br_train);
// System.out.println(inst_train.numAttributes());
// inst_train.setClassIndex(inst_train.numAttributes()-1);
// System.out.println("train time"+(System.currentTimeMillis()-st_time));
// NaiveBayes nb1 = new NaiveBayes();
// nb1.buildClassifier(inst_train);
// br_train.close();
long st_time = System.currentTimeMillis();
st_time = System.currentTimeMillis();
Classifier classifier = (Classifier) SerializationHelper.read("NaiveBayes.model");
// BufferedReader br_test = new BufferedReader(new FileReader("src/test.arff.txt"));
// Instances inst_test = new Instances(br_test);
// inst_test.setClassIndex(inst_test.numAttributes()-1);
// System.out.println("test time"+(System.currentTimeMillis()-st_time));
//
ArffLoader testLoader = new ArffLoader();
testLoader.setSource(new File("src/test.arff"));
testLoader.setRetrieval(Loader.BATCH);
Instances testDataSet = testLoader.getDataSet();
Attribute testAttribute = testDataSet.attribute("class");
testDataSet.setClass(testAttribute);
int correct = 0;
int incorrect = 0;
FastVector attInfo = new FastVector();
attInfo.addElement(new Attribute("Id"));
attInfo.addElement(new Attribute("Category"));
Instances outputInstances = new Instances("predict", attInfo, testDataSet.numInstances());
Enumeration testInstances = testDataSet.enumerateInstances();
int index = 1;
while (testInstances.hasMoreElements()) {
Instance instance = (Instance) testInstances.nextElement();
double classification = classifier.classifyInstance(instance);
Instance predictInstance = new Instance(outputInstances.numAttributes());
predictInstance.setValue(0, index++);
predictInstance.setValue(1, (int) classification + 1);
outputInstances.add(predictInstance);
}
System.out.println("Correct Instance: " + correct);
System.out.println("IncCorrect Instance: " + incorrect);
double accuracy = (double) (correct) / (double) (correct + incorrect);
System.out.println("Accuracy: " + accuracy);
CSVSaver predictedCsvSaver = new CSVSaver();
predictedCsvSaver.setFile(new File("predict.csv"));
predictedCsvSaver.setInstances(outputInstances);
predictedCsvSaver.writeBatch();
System.out.println("Prediciton saved to predict.csv");
}
