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 saveInstances(Instances dataSet, String fileName) throws Exception {
ArffSaver saver = new ArffSaver();
saver.setInstances(dataSet);
saver.setFile(new File(fileName));
// saver.setDestination(new File("./data/test.arff")); // **not** necessary in 3.5.4 and later
saver.writeBatch();
}
public void saveInstances() {
if (isExternalStorageAvailable()) {
try {
ArffSaver saver = new ArffSaver();
saver.setInstances(instances);
saver.setFile(file);
saver.writeBatch();
} catch (IOException e) {
e.printStackTrace();
}
}
}
public static void main(String[] args) throws Exception {
if (args.length != 2) {
System.err.println("Usage: FeatureGenerator input-badges-file features-file");
System.exit(-1);
}
Instances data = readData(args[0]);
ArffSaver saver = new ArffSaver();
saver.setInstances(data);
saver.setFile(new File(args[1]));
saver.writeBatch();
}
protected static void pca(Instances data) throws Exception {
PrincipalComponents pca = new PrincipalComponents();
pca.setInputFormat(data);
pca.setMaximumAttributes(4);
Instances newData = Filter.useFilter(data, pca);
System.out.println("newdata" + newData);
ArffSaver saver = new ArffSaver();
saver.setInstances(newData);
saver.setFile(new File("PCAresult.arff"));
saver.writeBatch();
}
public static void main(String[] args) throws Exception {
if (args.length != 3)
throw new IllegalArgumentException(
"Required parameters: <input> <attribute_indices> <output>");
System.out.println("Loading input data: " + args[0]);
Instances input = DataSource.read(args[0]);
System.out.println("Applying filter using indices: " + args[1]);
MekaClassAttributes filter = new MekaClassAttributes();
filter.setAttributeIndices(args[1]);
filter.setInputFormat(input);
Instances output = Filter.useFilter(input, filter);
System.out.println("Saving filtered data to: " + args[2]);
ArffSaver saver = new ArffSaver();
saver.setFile(new File(args[2]));
DataSink.write(saver, output);
}
public QSARModel train(Instances data) throws QSARException {
// GET A UUID AND DEFINE THE TEMPORARY FILE WHERE THE TRAINING DATA
// ARE STORED IN ARFF FORMAT PRIOR TO TRAINING.
final String rand = java.util.UUID.randomUUID().toString();
final String temporaryFilePath = ServerFolders.temp + "/" + rand + ".arff";
final File tempFile = new File(temporaryFilePath);
// SAVE THE DATA IN THE TEMPORARY FILE
try {
ArffSaver dataSaver = new ArffSaver();
dataSaver.setInstances(data);
dataSaver.setDestination(new FileOutputStream(tempFile));
dataSaver.writeBatch();
if (!tempFile.exists()) {
throw new IOException("Temporary File was not created");
}
} catch (final IOException ex) {
/*
* The content of the dataset cannot be
* written to the destination file due to
* some communication issue.
*/
tempFile.delete();
throw new RuntimeException(
"Unexpected condition while trying to save the " + "dataset in a temporary ARFF file",
ex);
}
NaiveBayes classifier = new NaiveBayes();
String[] generalOptions = {
"-c",
Integer.toString(data.classIndex() + 1),
"-t",
temporaryFilePath,
/// Save the model in the following directory
"-d",
ServerFolders.models_weka + "/" + uuid
};
try {
Evaluation.evaluateModel(classifier, generalOptions);
} catch (final Exception ex) {
tempFile.delete();
throw new QSARException(
Cause.XQReg350,
"Unexpected condition while trying to train "
+ "an SVM model. Possible explanation : {"
+ ex.getMessage()
+ "}",
ex);
}
QSARModel model = new QSARModel();
model.setParams(getParameters());
model.setCode(uuid.toString());
model.setAlgorithm(YaqpAlgorithms.NAIVE_BAYES);
model.setDataset(datasetUri);
model.setModelStatus(ModelStatus.UNDER_DEVELOPMENT);
ArrayList<Feature> independentFeatures = new ArrayList<Feature>();
for (int i = 0; i < data.numAttributes(); i++) {
Feature f = new Feature(data.attribute(i).name());
if (data.classIndex() != i) {
independentFeatures.add(f);
}
}
Feature dependentFeature = new Feature(data.classAttribute().name());
Feature predictedFeature = dependentFeature;
model.setDependentFeature(dependentFeature);
model.setIndependentFeatures(independentFeatures);
model.setPredictionFeature(predictedFeature);
tempFile.delete();
return model;
}
