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opennlp.tools.ml.naivebayes.NaiveBayesModel Maven / Gradle / Ivy
/*
* 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.tools.ml.naivebayes;
import java.io.BufferedReader;
import java.io.File;
import java.io.InputStreamReader;
import java.text.DecimalFormat;
import java.util.Map;
import opennlp.tools.ml.model.AbstractModel;
import opennlp.tools.ml.model.Context;
import opennlp.tools.ml.model.EvalParameters;
/**
* Class implementing the multinomial Naive Bayes classifier model.
*/
public class NaiveBayesModel extends AbstractModel {
protected double[] outcomeTotals;
protected long vocabulary;
public NaiveBayesModel(Context[] params, String[] predLabels, Map pmap, String[] outcomeNames) {
super(params, predLabels, pmap, outcomeNames);
outcomeTotals = initOutcomeTotals(outcomeNames, params);
this.evalParams = new NaiveBayesEvalParameters(params, outcomeNames.length, outcomeTotals, predLabels.length);
modelType = ModelType.NaiveBayes;
}
public NaiveBayesModel(Context[] params, String[] predLabels, String[] outcomeNames) {
super(params, predLabels, outcomeNames);
outcomeTotals = initOutcomeTotals(outcomeNames, params);
this.evalParams = new NaiveBayesEvalParameters(params, outcomeNames.length, outcomeTotals, predLabels.length);
modelType = ModelType.NaiveBayes;
}
protected double[] initOutcomeTotals(String[] outcomeNames, Context[] params) {
double[] outcomeTotals = new double[outcomeNames.length];
for (int i = 0; i < params.length; ++i) {
Context context = params[i];
for (int j = 0; j < context.getOutcomes().length; ++j) {
int outcome = context.getOutcomes()[j];
double count = context.getParameters()[j];
outcomeTotals[outcome] += count;
}
}
return outcomeTotals;
}
public double[] eval(String[] context) {
return eval(context, new double[evalParams.getNumOutcomes()]);
}
public double[] eval(String[] context, float[] values) {
return eval(context, values, new double[evalParams.getNumOutcomes()]);
}
public double[] eval(String[] context, double[] probs) {
return eval(context, null, probs);
}
public double[] eval(String[] context, float[] values, double[] outsums) {
int[] scontexts = new int[context.length];
java.util.Arrays.fill(outsums, 0);
for (int i = 0; i < context.length; i++) {
Integer ci = pmap.get(context[i]);
scontexts[i] = ci == null ? -1 : ci;
}
return eval(scontexts, values, outsums, evalParams, true);
}
public static double[] eval(int[] context, double[] prior, EvalParameters model) {
return eval(context, null, prior, model, true);
}
public static double[] eval(int[] context, float[] values, double[] prior, EvalParameters model, boolean normalize) {
Probabilities probabilities = new LogProbabilities<>();
Context[] params = model.getParams();
double[] outcomeTotals = model instanceof NaiveBayesEvalParameters ? ((NaiveBayesEvalParameters) model).getOutcomeTotals() : new double[prior.length];
long vocabulary = model instanceof NaiveBayesEvalParameters ? ((NaiveBayesEvalParameters) model).getVocabulary() : 0;
double[] activeParameters;
int[] activeOutcomes;
double value = 1;
for (int ci = 0; ci < context.length; ci++) {
if (context[ci] >= 0) {
Context predParams = params[context[ci]];
activeOutcomes = predParams.getOutcomes();
activeParameters = predParams.getParameters();
if (values != null) {
value = values[ci];
}
int ai = 0;
for (int i = 0; i < outcomeTotals.length && ai < activeOutcomes.length; ++i) {
int oid = activeOutcomes[ai];
double numerator = oid == i ? activeParameters[ai++] * value : 0;
double denominator = outcomeTotals[i];
probabilities.addIn(i, getProbability(numerator, denominator, vocabulary, true), 1);
}
}
}
double total = 0;
for (int i = 0; i < outcomeTotals.length; ++i) {
total += outcomeTotals[i];
}
for (int i = 0; i < outcomeTotals.length; ++i) {
double numerator = outcomeTotals[i];
probabilities.addIn(i, numerator / total, 1);
}
for (int i = 0; i < outcomeTotals.length; ++i) {
prior[i] = probabilities.get(i);
}
return prior;
}
private static double getProbability(double numerator, double denominator, double vocabulary, boolean isSmoothed) {
if (isSmoothed)
return getSmoothedProbability(numerator, denominator, vocabulary);
else if (denominator == 0 || denominator < Double.MIN_VALUE)
return 0;
else
return 1.0 * numerator / denominator;
}
private static double getSmoothedProbability(double numerator, double denominator, double vocabulary) {
final double delta = 0.05; // Lidstone smoothing
return 1.0 * (numerator + delta) / (denominator + delta * vocabulary);
}
public static void main(String[] args) throws java.io.IOException {
if (args.length == 0) {
System.err.println("Usage: NaiveBayesModel modelname < contexts");
System.exit(1);
}
AbstractModel m = new NaiveBayesModelReader(new File(args[0])).getModel();
BufferedReader in = new BufferedReader(new InputStreamReader(System.in));
DecimalFormat df = new java.text.DecimalFormat(".###");
for (String line = in.readLine(); line != null; line = in.readLine()) {
String[] context = line.split(" ");
double[] dist = m.eval(context);
for (int oi = 0; oi < dist.length; oi++) {
System.out.print("[" + m.getOutcome(oi) + " " + df.format(dist[oi]) + "] ");
}
System.out.println();
}
}
}
