public static void demonstrateSerialization() throws IOException, ClassNotFoundException {
System.out.println("Demonstrating working with a serialized classifier");
ColumnDataClassifier cdc = new ColumnDataClassifier("examples/cheese2007.prop");
Classifier<String, String> cl =
cdc.makeClassifier(cdc.readTrainingExamples("examples/cheeseDisease.train"));
// Exhibit serialization and deserialization working. Serialized to bytes in memory for
// simplicity
System.out.println();
System.out.println();
ByteArrayOutputStream baos = new ByteArrayOutputStream();
ObjectOutputStream oos = new ObjectOutputStream(baos);
oos.writeObject(cl);
oos.close();
byte[] object = baos.toByteArray();
ByteArrayInputStream bais = new ByteArrayInputStream(object);
ObjectInputStream ois = new ObjectInputStream(bais);
LinearClassifier<String, String> lc = ErasureUtils.uncheckedCast(ois.readObject());
ois.close();
ColumnDataClassifier cdc2 = new ColumnDataClassifier("examples/cheese2007.prop");
// We compare the output of the deserialized classifier lc versus the original one cl
// For both we use a ColumnDataClassifier to convert text lines to examples
for (String line : ObjectBank.getLineIterator("examples/cheeseDisease.test", "utf-8")) {
Datum<String, String> d = cdc.makeDatumFromLine(line);
Datum<String, String> d2 = cdc2.makeDatumFromLine(line);
System.out.println(line + " =origi=> " + cl.classOf(d));
System.out.println(line + " =deser=> " + lc.classOf(d2));
}
}
public static void main(String[] args) throws Exception {
ColumnDataClassifier cdc = new ColumnDataClassifier("examples/cheese2007.prop");
Classifier<String, String> cl =
cdc.makeClassifier(cdc.readTrainingExamples("examples/cheeseDisease.train"));
for (String line : ObjectBank.getLineIterator("examples/cheeseDisease.test", "utf-8")) {
// instead of the method in the line below, if you have the individual elements
// already you can use cdc.makeDatumFromStrings(String[])
Datum<String, String> d = cdc.makeDatumFromLine(line);
System.out.println(line + " ==> " + cl.classOf(d));
}
demonstrateSerialization();
}
public <F> double score(Classifier<L, F> classifier, GeneralDataset<L, F> data) {
List<L> guesses = new ArrayList<L>();
List<L> labels = new ArrayList<L>();
for (int i = 0; i < data.size(); i++) {
Datum<L, F> d = data.getRVFDatum(i);
L guess = classifier.classOf(d);
guesses.add(guess);
}
int[] labelsArr = data.getLabelsArray();
labelIndex = data.labelIndex;
for (int i = 0; i < data.size(); i++) {
labels.add(labelIndex.get(labelsArr[i]));
}
labelIndex = new HashIndex<L>();
labelIndex.addAll(data.labelIndex().objectsList());
labelIndex.addAll(classifier.labels());
int numClasses = labelIndex.size();
tpCount = new int[numClasses];
fpCount = new int[numClasses];
fnCount = new int[numClasses];
negIndex = labelIndex.indexOf(negLabel);
for (int i = 0; i < guesses.size(); ++i) {
L guess = guesses.get(i);
int guessIndex = labelIndex.indexOf(guess);
L label = labels.get(i);
int trueIndex = labelIndex.indexOf(label);
if (guessIndex == trueIndex) {
if (guessIndex != negIndex) {
tpCount[guessIndex]++;
}
} else {
if (guessIndex != negIndex) {
fpCount[guessIndex]++;
}
if (trueIndex != negIndex) {
fnCount[trueIndex]++;
}
}
}
return getFMeasure();
}
public <F> double score(Classifier<L, F> classifier, GeneralDataset<L, F> data) {
labelIndex = new HashIndex<L>();
labelIndex.addAll(classifier.labels());
labelIndex.addAll(data.labelIndex.objectsList());
clearCounts();
int[] labelsArr = data.getLabelsArray();
for (int i = 0; i < data.size(); i++) {
Datum<L, F> d = data.getRVFDatum(i);
L guess = classifier.classOf(d);
addGuess(guess, labelIndex.get(labelsArr[i]));
}
finalizeCounts();
return getFMeasure();
}
