package org.maltparser.ml.lib;
import java.io.BufferedReader;
import java.io.EOFException;
import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import java.io.Reader;
import java.io.Serializable;
import java.nio.charset.Charset;
import java.util.Arrays;
import java.util.regex.Pattern;
import org.maltparser.core.helper.Util;
import de.bwaldvogel.liblinear.SolverType;
/**
* <p>This class borrows code from liblinear.Model.java of the Java implementation of the liblinear package.
* MaltLiblinearModel stores the model obtained from the training procedure. In addition to the original code the model is more integrated to
* MaltParser. Instead of moving features from MaltParser's internal data structures to liblinear's data structure it uses MaltParser's data
* structure directly on the model. </p>
*
* @author Johan Hall
*
*/
public class MaltLiblinearModel implements Serializable, MaltLibModel {
private static final long serialVersionUID = 7526471155622776147L;
private static final Charset FILE_CHARSET = Charset.forName("ISO-8859-1");
private double bias;
/** label of each class */
private int[] labels;
private int nr_class;
private int nr_feature;
private SolverType solverType;
/** feature weight array */
private double[][] w;
public MaltLiblinearModel(int[] labels, int nr_class, int nr_feature, double[][] w, SolverType solverType) {
this.labels = labels;
this.nr_class = nr_class;
this.nr_feature = nr_feature;
this.w = w;
this.solverType = solverType;
}
public MaltLiblinearModel(Reader inputReader) throws IOException {
loadModel(inputReader);
}
public MaltLiblinearModel(File modelFile) throws IOException {
BufferedReader inputReader = new BufferedReader(new InputStreamReader(new FileInputStream(modelFile), FILE_CHARSET));
loadModel(inputReader);
}
/**
* @return number of classes
*/
public int getNrClass() {
return nr_class;
}
/**
* @return number of features
*/
public int getNrFeature() {
return nr_feature;
}
public int[] getLabels() {
return Util.copyOf(labels, nr_class);
}
/**
* The nr_feature*nr_class array w gives feature weights. We use one
* against the rest for multi-class classification, so each feature
* index corresponds to nr_class weight values. Weights are
* organized in the following way
*
* <pre>
* +------------------+------------------+------------+
* | nr_class weights | nr_class weights | ...
* | for 1st feature | for 2nd feature |
* +------------------+------------------+------------+
* </pre>
*
* If bias >= 0, x becomes [x; bias]. The number of features is
* increased by one, so w is a (nr_feature+1)*nr_class array. The
* value of bias is stored in the variable bias.
* @see #getBias()
* @return a <b>copy of</b> the feature weight array as described
*/
// public double[] getFeatureWeights() {
// return Util.copyOf(w, w.length);
// }
/**
* @return true for logistic regression solvers
*/
public boolean isProbabilityModel() {
return (solverType == SolverType.L2R_LR || solverType == SolverType.L2R_LR_DUAL || solverType == SolverType.L1R_LR);
}
public double getBias() {
return bias;
}
public int[] predict(MaltFeatureNode[] x) {
final double[] dec_values = new double[nr_class];
final int[] predictionList = Util.copyOf(labels, nr_class);
final int n = (bias >= 0)?nr_feature + 1:nr_feature;
// final int nr_w = (nr_class == 2 && solverType != SolverType.MCSVM_CS)?1:nr_class;
final int xlen = x.length;
// int i;
// for (i = 0; i < nr_w; i++) {
// dec_values[i] = 0;
// }
for (int i=0; i < xlen; i++) {
if (x[i].index <= n) {
final int t = (x[i].index - 1);
if (w[t] != null) {
for (int j = 0; j < w[t].length; j++) {
dec_values[j] += w[t][j] * x[i].value;
}
}
}
}
double tmpDec;
int tmpObj;
int lagest;
final int nc = nr_class-1;
for (int i=0; i < nc; i++) {
lagest = i;
for (int j=i; j < nr_class; j++) {
if (dec_values[j] > dec_values[lagest]) {
lagest = j;
}
}
tmpDec = dec_values[lagest];
dec_values[lagest] = dec_values[i];
dec_values[i] = tmpDec;
tmpObj = predictionList[lagest];
predictionList[lagest] = predictionList[i];
predictionList[i] = tmpObj;
}
return predictionList;
}
private void readObject(ObjectInputStream is) throws ClassNotFoundException, IOException {
is.defaultReadObject();
}
private void writeObject(ObjectOutputStream os) throws IOException {
os.defaultWriteObject();
}
private void loadModel(Reader inputReader) throws IOException {
labels = null;
Pattern whitespace = Pattern.compile("\\s+");
BufferedReader reader = null;
if (inputReader instanceof BufferedReader) {
reader = (BufferedReader)inputReader;
} else {
reader = new BufferedReader(inputReader);
}
try {
String line = null;
while ((line = reader.readLine()) != null) {
String[] split = whitespace.split(line);
if (split[0].equals("solver_type")) {
SolverType solver = SolverType.valueOf(split[1]);
if (solver == null) {
throw new RuntimeException("unknown solver type");
}
solverType = solver;
} else if (split[0].equals("nr_class")) {
nr_class = Util.atoi(split[1]);
Integer.parseInt(split[1]);
} else if (split[0].equals("nr_feature")) {
nr_feature = Util.atoi(split[1]);
} else if (split[0].equals("bias")) {
bias = Util.atof(split[1]);
} else if (split[0].equals("w")) {
break;
} else if (split[0].equals("label")) {
labels = new int[nr_class];
for (int i = 0; i < nr_class; i++) {
labels[i] = Util.atoi(split[i + 1]);
}
} else {
throw new RuntimeException("unknown text in model file: [" + line + "]");
}
}
int w_size = nr_feature;
if (bias >= 0) w_size++;
int nr_w = nr_class;
if (nr_class == 2 && solverType != SolverType.MCSVM_CS) nr_w = 1;
w = new double[w_size][nr_w];
int[] buffer = new int[128];
for (int i = 0; i < w_size; i++) {
for (int j = 0; j < nr_w; j++) {
int b = 0;
while (true) {
int ch = reader.read();
if (ch == -1) {
throw new EOFException("unexpected EOF");
}
if (ch == ' ') {
w[i][j] = Util.atof(new String(buffer, 0, b));
break;
} else {
buffer[b++] = ch;
}
}
}
}
}
finally {
Util.closeQuietly(reader);
}
}
public int hashCode() {
final int prime = 31;
long temp = Double.doubleToLongBits(bias);
int result = prime * 1 + (int)(temp ^ (temp >>> 32));
result = prime * result + Arrays.hashCode(labels);
result = prime * result + nr_class;
result = prime * result + nr_feature;
result = prime * result + ((solverType == null) ? 0 : solverType.hashCode());
for (int i = 0; i < w.length; i++) {
result = prime * result + Arrays.hashCode(w[i]);
}
return result;
}
public boolean equals(Object obj) {
if (this == obj) return true;
if (obj == null) return false;
if (getClass() != obj.getClass()) return false;
MaltLiblinearModel other = (MaltLiblinearModel)obj;
if (Double.doubleToLongBits(bias) != Double.doubleToLongBits(other.bias)) return false;
if (!Arrays.equals(labels, other.labels)) return false;
if (nr_class != other.nr_class) return false;
if (nr_feature != other.nr_feature) return false;
if (solverType == null) {
if (other.solverType != null) return false;
} else if (!solverType.equals(other.solverType)) return false;
for (int i = 0; i < w.length; i++) {
if (other.w.length <= i) return false;
if (!Util.equals(w[i], other.w[i])) return false;
}
return true;
}
public String toString() {
final StringBuilder sb = new StringBuilder("Model");
sb.append(" bias=").append(bias);
sb.append(" nr_class=").append(nr_class);
sb.append(" nr_feature=").append(nr_feature);
sb.append(" solverType=").append(solverType);
return sb.toString();
}
}