private int[] prepareSplit(
String sampleFile,
String featureDefFile,
double percentTrain,
boolean normalize,
List<RankList> trainingData,
List<RankList> testData) {
List<RankList> data = readInput(sampleFile); // read input
int[] features = readFeature(featureDefFile); // read features
if (features == null) // no features specified ==> use all features in the training file
features = getFeatureFromSampleVector(data);
if (normalize) normalize(data, features);
if (newFeatureFile.compareTo("") != 0) {
System.out.print("Loading new feature description file... ");
List<String> descriptions = FileUtils.readLine(newFeatureFile, "ASCII");
for (int i = 0; i < descriptions.size(); i++) {
if (descriptions.get(i).indexOf("##") == 0) continue;
LinearComputer lc = new LinearComputer("", descriptions.get(i));
// if we keep the orig. features ==> discard size-1 linear computer
if (!keepOrigFeatures || lc.size() > 1) lcList.add(lc);
}
features = applyNewFeatures(data, features);
System.out.println("[Done]");
}
int size = (int) (data.size() * percentTrain);
for (int i = 0; i < size; i++) trainingData.add(new RankList(data.get(i)));
for (int i = size; i < data.size(); i++) testData.add(new RankList(data.get(i)));
return features;
}
private int[] prepareCV(
String sampleFile,
String featureDefFile,
int nFold,
boolean normalize,
List<List<RankList>> trainingData,
List<List<RankList>> testData) {
List<RankList> data = readInput(sampleFile); // read input
int[] features = readFeature(featureDefFile); // read features
if (features == null) // no features specified ==> use all features in the training file
features = getFeatureFromSampleVector(data);
if (normalize) normalize(data, features);
if (newFeatureFile.compareTo("") != 0) {
System.out.print("Loading new feature description file... ");
List<String> descriptions = FileUtils.readLine(newFeatureFile, "ASCII");
for (int i = 0; i < descriptions.size(); i++) {
if (descriptions.get(i).indexOf("##") == 0) continue;
LinearComputer lc = new LinearComputer("", descriptions.get(i));
// if we keep the orig. features ==> discard size-1 linear computer
if (!keepOrigFeatures || lc.size() > 1) lcList.add(lc);
}
features = applyNewFeatures(data, features);
System.out.println("[Done]");
}
List<List<Integer>> trainSamplesIdx = new ArrayList<List<Integer>>();
int size = data.size() / nFold;
int start = 0;
int total = 0;
for (int f = 0; f < nFold; f++) {
List<Integer> t = new ArrayList<Integer>();
for (int i = 0; i < size && start + i < data.size(); i++) t.add(start + i);
trainSamplesIdx.add(t);
total += t.size();
start += size;
}
for (; total < data.size(); total++) trainSamplesIdx.get(trainSamplesIdx.size() - 1).add(total);
for (int i = 0; i < trainSamplesIdx.size(); i++) {
List<RankList> train = new ArrayList<RankList>();
List<RankList> test = new ArrayList<RankList>();
List<Integer> t = trainSamplesIdx.get(i);
for (int j = 0; j < data.size(); j++) {
if (t.contains(j)) test.add(new RankList(data.get(j)));
else train.add(new RankList(data.get(j)));
}
trainingData.add(train);
testData.add(test);
}
return features;
}
