public static void eval(MaxentModel model, Reader r, Evalable e, bool verbose)
{
float totPos = 0, truePos = 0, falsePos = 0;
Event[] events = (e.getEventCollector(r)).getEvents(true);
//MaxentModel model = e.getModel(dir, name);
string negOutcome = e.NegativeOutcome;
foreach (Event @event in events)
{
string guess = model.getBestOutcome(model.eval(@event.Context));
string ans = @event.Outcome;
if (verbose)
{
Console.WriteLine(ans + " " + guess);
}
if (!ans.Equals(negOutcome))
{
totPos++;
}
if (!guess.Equals(negOutcome) && !guess.Equals(ans))
{
falsePos++;
}
else if (ans.Equals(guess))
{
truePos++;
}
}
Console.WriteLine("Precision: " + truePos / (truePos + falsePos));
Console.WriteLine("Recall: " + truePos / totPos);
}
public static void run(string[] args, Evalable e)
{
// TOM: Was commented out to remove dependency on gnu getopt.
// String dir = "./";
// String stem = "maxent";
// int cutoff = 0; // default to no cutoff
// boolean train = false;
// boolean verbose = false;
// boolean local = false;
// gnu.getopt.Getopt g =
// new gnu.getopt.Getopt("maxent", args, "d:s:c:tvl");
// int c;
// while ((c = g.getopt()) != -1) {
// switch(c) {
// case 'd':
// dir = g.getOptarg()+"/";
// break;
// case 's':
// stem = g.getOptarg();
// break;
// case 'c':
// cutoff = Integer.parseInt(g.getOptarg());
// break;
// case 't':
// train = true;
// break;
// case 'l':
// local = true;
// break;
// case 'v':
// verbose = true;
// break;
// }
// }
//
// int lastIndex = g.getOptind();
// if (lastIndex >= args.length) {
// System.out.println("This is a usage message from opennlp.maxent.TrainEval. You have called the training procedure for a maxent application with the incorrect arguments. These are the options:");
//
// System.out.println("\nOptions for defining the model location and name:");
// System.out.println(" -d <directoryName>");
// System.out.println("\tThe directory in which to store the model.");
// System.out.println(" -s <modelName>");
// System.out.println("\tThe name of the model, e.g. EnglishPOS.bin.gz or NameFinder.txt.");
//
// System.out.println("\nOptions for training:");
// System.out.println(" -c <cutoff>");
// System.out.println("\tAn integer cutoff level to reduce infrequent contextual predicates.");
// System.out.println(" -t\tTrain a model. If absent, the given model will be loaded and evaluated.");
// System.out.println("\nOptions for evaluation:");
// System.out.println(" -l\t the evaluation method of class that uses the model. If absent, TrainEval's eval method is used.");
// System.out.println(" -v\t verbose.");
// System.out.println("\nThe final argument is the data file to be loaded and used for either training or evaluation.");
// System.out.println("\nAs an example for training:\n java opennlp.grok.preprocess.postag.POSTaggerME -t -d ./ -s EnglishPOS.bin.gz -c 7 postag.data");
// System.exit(0);
// }
//
// FileReader datafr = new FileReader(args[lastIndex]);
//
// if (train) {
// MaxentModel m =
// train(new EventCollectorAsStream(e.getEventCollector(datafr)),
// cutoff);
// new SuffixSensitiveGISModelWriter((AbstractModel)m,
// new File(dir+stem)).persist();
// }
// else {
// MaxentModel model =
// new SuffixSensitiveGISModelReader(new File(dir+stem)).getModel();
// if (local) {
// e.localEval(model, datafr, e, verbose);
// } else {
// eval(model, datafr, e, verbose);
// }
// }
}
public static void eval(MaxentModel model, Reader r, Evalable e)
{
eval(model, r, e, false);
}
