public static List<DrugUser> make_from_NEP(int target_count, int rng_seed) throws Exception {
HashMap<String, Object> population_params = load_defaults(null);
// RawLoader rl = new RawLoader(population_params);
// Instances hcv_learning_data = buildLearningInstances(rl.getLearningData());
// System.out.println(hcv_learning_data.toSummaryString());
// System.out.println(hcv_learning_data.toString());
// System.exit(1);
// HashMap <String, Classifier> classifiers = train_classifiers(hcv_learning_data);
PersonGenerator pg =
PersonGenerator.make_NEP_generator(
population_params, (Double) population_params.get("idu_maturity_threshold"), rng_seed);
HashMap<String, Object> generator_params = new HashMap<String, Object>();
generator_params.put("early_idus_only", (Boolean) false);
ArrayList<DrugUser> pop = new ArrayList<DrugUser>();
for (int idu_num = 0; idu_num < target_count; ++idu_num) {
try {
pop.add(pg.generate(generator_params));
System.out.print(".");
} catch (Exception e) {
System.out.println("x");
}
}
System.out.println("Synthetic population:" + pop.size());
if (pop.size() > 0) {
int num_infected = 0;
int num_abpos = 0;
for (DrugUser idu : pop) {
num_abpos += idu.isHcvABpos() ? 1 : 0;
}
System.out.println(
System.lineSeparator()
+ "Initial HCV prevalence (AB): "
+ num_abpos / (1.0 * pop.size()));
}
return pop;
}
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());
}