public static void analyze_accuracy_NHBS(int rng_seed) throws Exception {
HashMap<String, Object> population_params = load_defaults(null);
RawLoader rl = new RawLoader(population_params, true, false, rng_seed);
List<DrugUser> learningData = rl.getLearningData();
Instances nhbs_data =
new Instances("learning_instances", DrugUser.getAttInfo(), learningData.size());
for (DrugUser du : learningData) {
nhbs_data.add(du.getInstance());
}
System.out.println(nhbs_data.toSummaryString());
nhbs_data.setClass(DrugUser.getAttribMap().get("hcv_state"));
// wishlist: remove infrequent values
// weka.filters.unsupervised.instance.RemoveFrequentValues()
Filter f1 = new RemoveUseless();
f1.setInputFormat(nhbs_data);
nhbs_data = Filter.useFilter(nhbs_data, f1);
System.out.println("NHBS IDU 2009 Dataset");
System.out.println("Summary of input:");
// System.out.printlnnhbs_data.toSummaryString());
System.out.println(" Num of classes: " + nhbs_data.numClasses());
System.out.println(" Num of attributes: " + nhbs_data.numAttributes());
for (int idx = 0; idx < nhbs_data.numAttributes(); ++idx) {
Attribute attr = nhbs_data.attribute(idx);
System.out.println("" + idx + ": " + attr.toString());
System.out.println(" distinct values:" + nhbs_data.numDistinctValues(idx));
// System.out.println("" + attr.enumerateValues());
}
ArrayList<String> options = new ArrayList<String>();
options.add("-Q");
options.add("" + rng_seed);
// System.exit(0);
// nhbs_data.deleteAttributeAt(0); //response ID
// nhbs_data.deleteAttributeAt(16); //zip
// Classifier classifier = new NNge(); //best nearest-neighbor classifier: 40.00
// ROC=0.60
// Classifier classifier = new MINND();
// Classifier classifier = new CitationKNN();
// Classifier classifier = new LibSVM(); //requires LibSVM classes. only gets 37.7%
// Classifier classifier = new SMOreg();
Classifier classifier = new Logistic();
// ROC=0.686
// Classifier classifier = new LinearNNSearch();
// LinearRegression: Cannot handle multi-valued nominal class!
// Classifier classifier = new LinearRegression();
// Classifier classifier = new RandomForest();
// String[] options = {"-I", "100", "-K", "4"}; //-I trees, -K features per tree. generally,
// might want to optimize (or not
// https://cwiki.apache.org/confluence/display/MAHOUT/Random+Forests)
// options.add("-I"); options.add("100"); options.add("-K"); options.add("4");
// ROC=0.673
// KStar classifier = new KStar();
// classifier.setGlobalBlend(20); //the amount of not greedy, in percent
// ROC=0.633
// Classifier classifier = new AdaBoostM1();
// ROC=0.66
// Classifier classifier = new MultiBoostAB();
// ROC=0.67
// Classifier classifier = new Stacking();
// ROC=0.495
// J48 classifier = new J48(); // new instance of tree //building a C45 tree classifier
// ROC=0.585
// String[] options = new String[1];
// options[0] = "-U"; // unpruned tree
// classifier.setOptions(options); // set the options
classifier.setOptions((String[]) options.toArray(new String[0]));
// not needed before CV: http://weka.wikispaces.com/Use+WEKA+in+your+Java+code
// classifier.buildClassifier(nhbs_data); // build classifier
// evaluation
Evaluation eval = new Evaluation(nhbs_data);
eval.crossValidateModel(classifier, nhbs_data, 10, new Random(1)); // 10-fold cross validation
System.out.println(eval.toSummaryString("\nResults\n\n", false));
System.out.println(eval.toClassDetailsString());
// System.out.println(eval.toCumulativeMarginDistributionString());
}
public void processFolder(File folder) throws Exception {
if (!folder.isDirectory()) {
// manipulate file here
String fileName = folder.getName();
System.out.println(fileName);
// String extension = getFileExtension(fileName);
testdata = new Instances(new BufferedReader(new FileReader(folder)));
if (!fileName.startsWith(".") && (fileName.contains(".csv") || fileName.contains(".xls"))) {
CSVLoader loader = new CSVLoader();
loader.setSource(new File(folder.getAbsolutePath()));
traindata = loader.getDataSet();
System.out.println(traindata.toSummaryString());
this.chooseClassifier();
} else if (!fileName.startsWith(".") && fileName.contains(".txt")) {
TextDirectoryLoader loader = new TextDirectoryLoader();
System.out.println("About to load text file " + fileName);
System.out.println("Name of path " + folder.getAbsolutePath());
loader.setSource(folder);
traindata = loader.getDataSet();
System.out.println(traindata.toSummaryString());
this.chooseClassifier();
} else if (!fileName.startsWith(".") && fileName.contains(".json")) {
JSONLoader loader = new JSONLoader();
loader.setSource(new File(folder.getAbsolutePath()));
traindata = loader.getDataSet();
System.out.println(traindata.toSummaryString());
this.chooseClassifier();
} else if (!fileName.startsWith(".") && fileName.contains(".xrff")) {
XRFFLoader loader = new XRFFLoader();
loader.setSource(new File(folder.getAbsolutePath()));
traindata = loader.getDataSet();
System.out.println(traindata.toSummaryString());
this.chooseClassifier();
} else if (!fileName.startsWith(".") && fileName.contains(".arff")) {
traindata = new Instances(new BufferedReader(new FileReader(folder.getAbsolutePath())));
testdata = new Instances(new BufferedReader(new FileReader(folder)));
System.out.println(traindata.toSummaryString());
this.chooseClassifier();
} else if (!fileName.startsWith(".") && fileName.contains(".mdf")) {
DatabaseConnection loader = new DatabaseConnection();
loader.connectToDatabase();
InstanceQuery query = new InstanceQuery();
query.setUsername("lamogha");
query.setPassword("l@mmyPHD");
query.setQuery("select * from customers");
// You can declare that your data set is sparse
// query.setSparseData(true);
Instances data = query.retrieveInstances();
System.out.println(data.toSummaryString());
this.chooseClassifier();
}
} else {
for (final File fileEntry : folder.listFiles()) {
if (fileEntry.isDirectory()) {
this.processFolder(fileEntry);
} else {
// manipulate file here
String fileName = fileEntry.getName();
System.out.println(fileName);
if (!fileName.startsWith(".")
&& (fileName.contains(".csv") || fileName.contains(".xls"))) {
CSVLoader loader = new CSVLoader();
loader.setSource(new File(fileEntry.getAbsolutePath()));
traindata = loader.getDataSet();
System.out.println(traindata.toSummaryString());
this.chooseClassifier();
} else if (!fileName.startsWith(".") && fileName.contains(".txt")) {
TextDirectoryLoader loader = new TextDirectoryLoader();
System.out.println("About to load text file " + fileName);
System.out.println("Name of path " + fileEntry.getAbsolutePath());
loader.setSource(folder);
traindata = loader.getDataSet();
System.out.println(traindata.toSummaryString());
this.chooseClassifier();
} else if (!fileName.startsWith(".") && fileName.contains(".json")) {
JSONLoader loader = new JSONLoader();
loader.setSource(new File(fileEntry.getAbsolutePath()));
traindata = loader.getDataSet();
System.out.println(traindata.toSummaryString());
this.chooseClassifier();
} else if (!fileName.startsWith(".") && fileName.contains(".xrff")) {
XRFFLoader loader = new XRFFLoader();
loader.setSource(new File(fileEntry.getAbsolutePath()));
traindata = loader.getDataSet();
System.out.println(traindata.toSummaryString());
this.chooseClassifier();
} else if (!fileName.startsWith(".")) {
traindata =
new Instances(new BufferedReader(new FileReader(fileEntry.getAbsolutePath())));
System.out.println(traindata.toSummaryString());
this.chooseClassifier();
} else if (!fileName.startsWith(".") && fileName.contains(".mdf")) {
DatabaseConnection loader = new DatabaseConnection();
loader.connectToDatabase();
InstanceQuery query = new InstanceQuery();
query.setUsername("lamogha");
query.setPassword("l@mmyPHD");
query.setQuery("select * from customers");
// You can declare that your data set is sparse
// query.setSparseData(true);
Instances data = query.retrieveInstances();
System.out.println(data.toSummaryString());
this.chooseClassifier();
}
}
}
// System.exit(0);
}
}