/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
package opennlp.maxent;
import java.io.File;
import java.io.FileReader;
import java.text.DecimalFormat;
import opennlp.model.Event;
import opennlp.model.EventStream;
import opennlp.model.GenericModelReader;
import opennlp.model.MaxentModel;
import opennlp.model.RealValueFileEventStream;
/**
* Test the model on some input.
*/
public class ModelApplier {
MaxentModel _model;
ContextGenerator _cg = new BasicContextGenerator(",");
int counter = 1;
// The format for printing percentages
public static final DecimalFormat ROUNDED_FORMAT = new DecimalFormat("0.000");
public ModelApplier(MaxentModel m) {
_model = m;
}
private void eval(Event event) {
eval(event, false);
}
private void eval(Event event, boolean real) {
String outcome = event.getOutcome(); // Is ignored
String[] context = event.getContext();
double[] ocs;
if (!real) {
ocs = _model.eval(context);
} else {
float[] values = RealValueFileEventStream.parseContexts(context);
ocs = _model.eval(context, values);
}
int numOutcomes = ocs.length;
DoubleStringPair[] result = new DoubleStringPair[numOutcomes];
for (int i=0; i<numOutcomes; i++)
result[i] = new DoubleStringPair(ocs[i], _model.getOutcome(i));
java.util.Arrays.sort(result);
// Print the most likely outcome first, down to the least likely.
for (int i=numOutcomes-1; i>=0; i--)
System.out.print(result[i].stringValue + " " + result[i].doubleValue + " ");
System.out.println();
}
private static void usage() {
System.err.println("java ModelApplier [-real] modelFile dataFile");
System.exit(1);
}
/**
* Main method. Call as follows:
* <p>
* java ModelApplier modelFile dataFile
*/
public static void main(String[] args) {
String dataFileName, modelFileName;
boolean real = false;
String type = "maxent";
int ai = 0;
if (args.length == 0) {
usage();
}
if (args.length > 0) {
while (args[ai].startsWith("-")) {
if (args[ai].equals("-real")) {
real = true;
} else if (args[ai].equals("-perceptron")) {
type = "perceptron";
} else {
usage();
}
ai++;
}
modelFileName = args[ai++];
dataFileName = args[ai++];
ModelApplier predictor = null;
try {
MaxentModel m = new GenericModelReader(new File(modelFileName)).getModel();
predictor = new ModelApplier(m);
} catch (Exception e) {
e.printStackTrace();
System.exit(0);
}
try {
EventStream es = new BasicEventStream(new PlainTextByLineDataStream(
new FileReader(new File(dataFileName))), ",");
while (es.hasNext())
predictor.eval(es.next(), real);
return;
} catch (Exception e) {
System.out.println("Unable to read from specified file: "
+ modelFileName);
System.out.println();
e.printStackTrace();
}
}
}
}