/**
* Copyright (c) 2010, Regents of the University of Colorado All rights
* reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer. Redistributions in binary
* form must reproduce the above copyright notice, this list of conditions and
* the following disclaimer in the documentation and/or other materials provided
* with the distribution. Neither the name of the University of Colorado at
* Boulder nor the names of its contributors may be used to endorse or promote
* products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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*/
package clear.engine;
import clear.decode.AbstractDecoder;
import clear.decode.BinaryDecoder;
import clear.decode.OneVsAllDecoder;
import clear.train.AbstractTrainer;
import clear.train.kernel.AbstractKernel;
import clear.util.DSUtil;
import clear.util.IOUtil;
import clear.util.tuple.JIntDoubleTuple;
import java.io.BufferedReader;
import java.io.PrintStream;
import org.kohsuke.args4j.CmdLineException;
import org.kohsuke.args4j.CmdLineParser;
import org.kohsuke.args4j.Option;
/**
* Predicts using a classifier.
*
* @author Jinho D. Choi <b>Last update:</b> 11/8/2010
*/
public class MLPredict {
@Option(name = "-i", usage = "input file", required = true, metaVar = "REQUIRED")
String s_inputFile;
@Option(name = "-o", usage = "output file", required = true, metaVar = "REQUIRED")
String s_outputFile;
@Option(name = "-m", usage = "model file", required = true, metaVar = "REQUIRED")
String s_modelFile;
@Option(name = "-s", usage = "strategy ::= " + AbstractTrainer.ST_BINARY + " (binary) | " + AbstractTrainer.ST_ONE_VS_ALL + " (one-vs-all; default)", metaVar = "OPTIONAL")
byte i_strategy = AbstractTrainer.ST_ONE_VS_ALL;
public MLPredict(String[] args) {
CmdLineParser cmd = new CmdLineParser(this);
try {
cmd.parseArgument(args);
long st = System.currentTimeMillis();
System.out.println("* Loading model: " + s_modelFile);
AbstractDecoder decode;
if (i_strategy == AbstractTrainer.ST_BINARY) {
decode = new BinaryDecoder(s_modelFile);
} else // One-vs-all
{
decode = new OneVsAllDecoder(s_modelFile);
}
BufferedReader fin = IOUtil.createBufferedFileReader(s_inputFile);
PrintStream fout = IOUtil.createPrintFileStream(s_outputFile);
String line;
String[] tok;
int y;
JIntDoubleTuple res;
int correct = 0, total;
System.out.println("* Predicting : " + s_inputFile);
for (total = 0; (line = fin.readLine()) != null; total++) {
tok = line.split(AbstractKernel.COL_DELIM);
y = Integer.parseInt(tok[0]);
if (!line.contains(":")) {
res = decode.predict(DSUtil.toIntArray(tok, 1));
} else {
res = decode.predict(DSUtil.toJIntDoubleArray(tok, 1));
}
fout.println(res.i + " " + res.d);
if (res.i == y) {
correct++;
}
}
fout.close();
long time = System.currentTimeMillis() - st;
System.out.printf("* Accuracy : %f (%d/%d)\n", (double) correct / total, correct, total);
System.out.printf("* Decoding time: %d hours, %d minutes, %d seconds\n", time / (1000 * 3600), time / (1000 * 60), time / 1000);
} catch (CmdLineException e) {
System.err.println(e.getMessage());
cmd.printUsage(System.err);
} catch (Exception e) {
e.printStackTrace();
}
}
static public void main(String[] args) {
MLPredict mlp = new MLPredict(args);
}
}