static void mainToOutput(String[] args, PrintWriter output) throws Exception {
if (parseArgs(args)) {
if (!showAuc && !showConfusion && !showScores) {
showAuc = true;
showConfusion = true;
}
Auc collector = new Auc();
LogisticModelParameters lmp = LogisticModelParameters.loadFrom(new File(modelFile));
CsvRecordFactory csv = lmp.getCsvRecordFactory();
OnlineLogisticRegression lr = lmp.createRegression();
BufferedReader in = TrainLogistic.open(inputFile);
String line = in.readLine();
csv.firstLine(line);
line = in.readLine();
if (showScores) {
output.println("\"target\",\"model-output\",\"log-likelihood\"");
}
while (line != null) {
Vector v = new SequentialAccessSparseVector(lmp.getNumFeatures());
int target = csv.processLine(line, v);
double score = lr.classifyScalar(v);
if (showScores) {
output.printf(
Locale.ENGLISH, "%d,%.3f,%.6f%n", target, score, lr.logLikelihood(target, v));
}
collector.add(target, score);
line = in.readLine();
}
if (showAuc) {
output.printf(Locale.ENGLISH, "AUC = %.2f%n", collector.auc());
}
if (showConfusion) {
Matrix m = collector.confusion();
output.printf(
Locale.ENGLISH,
"confusion: [[%.1f, %.1f], [%.1f, %.1f]]%n",
m.get(0, 0),
m.get(1, 0),
m.get(0, 1),
m.get(1, 1));
m = collector.entropy();
output.printf(
Locale.ENGLISH,
"entropy: [[%.1f, %.1f], [%.1f, %.1f]]%n",
m.get(0, 0),
m.get(1, 0),
m.get(0, 1),
m.get(1, 1));
}
}
}
@Override
public double getLoss(
List<? extends AbstractInstance> crossValSet, PredictiveModel predictiveModel) {
Auc auc = new Auc();
for (AbstractInstance instance : crossValSet) {
auc.add(
(Double) instance.getClassification() == 1.0 ? 1 : 0,
predictiveModel.getProbability(instance.getAttributes(), 1.0));
}
return 1 - auc.auc();
}