static void mainToOutput(String[] args, PrintWriter output) throws Exception {
if (!parseArgs(args)) {
return;
}
AdaptiveLogisticModelParameters lmp =
AdaptiveLogisticModelParameters.loadFromFile(new File(modelFile));
CsvRecordFactory csv = lmp.getCsvRecordFactory();
csv.setIdName(idColumn);
AdaptiveLogisticRegression lr = lmp.createAdaptiveLogisticRegression();
State<Wrapper, CrossFoldLearner> best = lr.getBest();
if (best == null) {
output.println("AdaptiveLogisticRegression has not be trained probably.");
return;
}
CrossFoldLearner learner = best.getPayload().getLearner();
BufferedReader in = TrainAdaptiveLogistic.open(inputFile);
BufferedWriter out =
new BufferedWriter(
new OutputStreamWriter(new FileOutputStream(outputFile), Charsets.UTF_8));
out.write(idColumn + ",target,score");
out.newLine();
String line = in.readLine();
csv.firstLine(line);
line = in.readLine();
Map<String, Double> results = new HashMap<String, Double>();
int k = 0;
while (line != null) {
Vector v = new SequentialAccessSparseVector(lmp.getNumFeatures());
csv.processLine(line, v, false);
Vector scores = learner.classifyFull(v);
results.clear();
if (maxScoreOnly) {
results.put(csv.getTargetLabel(scores.maxValueIndex()), scores.maxValue());
} else {
for (int i = 0; i < scores.size(); i++) {
results.put(csv.getTargetLabel(i), scores.get(i));
}
}
for (Map.Entry<String, Double> entry : results.entrySet()) {
out.write(csv.getIdString(line) + ',' + entry.getKey() + ',' + entry.getValue());
out.newLine();
}
k++;
if (k % 100 == 0) {
output.println(k + " records processed");
}
line = in.readLine();
}
out.flush();
out.close();
output.println(k + " records processed totally.");
}