Java tutorial
package com.netease.news.classifier.naivebayes; /** * 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. */ import java.io.DataInputStream; import java.io.IOException; import java.io.InputStream; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FSDataInputStream; import org.apache.hadoop.fs.FSDataOutputStream; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.mahout.math.DenseVector; import org.apache.mahout.math.Matrix; import org.apache.mahout.math.SparseRowMatrix; import org.apache.mahout.math.Vector; import org.apache.mahout.math.VectorWritable; import com.google.common.base.Preconditions; import com.google.common.io.Closeables; /** * NaiveBayesModel holds the weight Matrix, the feature and label sums and the * weight normalizer vectors. */ public class NaiveBayesModel { private final Vector weightsPerLabel; private final Vector perlabelThetaNormalizer; // private final double minThetaNormalizer; private final Vector weightsPerFeature; private final Matrix weightsPerLabelAndFeature; private final float alphaI; private final double numFeatures; private final double totalWeightSum; public NaiveBayesModel(Matrix weightMatrix, Vector weightsPerFeature, Vector weightsPerLabel, Vector thetaNormalizer, float alphaI) { this.weightsPerLabelAndFeature = weightMatrix; this.weightsPerFeature = weightsPerFeature; this.weightsPerLabel = weightsPerLabel; this.perlabelThetaNormalizer = thetaNormalizer; this.numFeatures = weightsPerFeature.getNumNondefaultElements(); this.totalWeightSum = weightsPerLabel.zSum(); this.alphaI = alphaI; // this.minThetaNormalizer = thetaNormalizer.maxValue(); } public double labelWeight(int label) { return weightsPerLabel.getQuick(label); } // public double thetaNormalizer(int label) { // return perlabelThetaNormalizer.get(label) / minThetaNormalizer; // } public double featureWeight(int feature) { return weightsPerFeature.getQuick(feature); } public double weight(int label, int feature) { return weightsPerLabelAndFeature.getQuick(label, feature); } public float alphaI() { return alphaI; } public double numFeatures() { return numFeatures; } public double totalWeightSum() { return totalWeightSum; } public int numLabels() { return weightsPerLabel.size(); } public Vector createScoringVector() { return weightsPerLabel.like(); } public static NaiveBayesModel materialize(Path output, Configuration conf) throws IOException { FileSystem fs = output.getFileSystem(conf); Vector weightsPerLabel = null; Vector perLabelThetaNormalizer = null; Vector weightsPerFeature = null; Matrix weightsPerLabelAndFeature; float alphaI; FSDataInputStream in = fs.open(new Path(output, "naiveBayesModel.bin")); try { alphaI = in.readFloat(); weightsPerFeature = VectorWritable.readVector(in); weightsPerLabel = new DenseVector(VectorWritable.readVector(in)); perLabelThetaNormalizer = new DenseVector(VectorWritable.readVector(in)); weightsPerLabelAndFeature = new SparseRowMatrix(weightsPerLabel.size(), weightsPerFeature.size()); for (int label = 0; label < weightsPerLabelAndFeature.numRows(); label++) { weightsPerLabelAndFeature.assignRow(label, VectorWritable.readVector(in)); } } finally { Closeables.close(in, true); } NaiveBayesModel model = new NaiveBayesModel(weightsPerLabelAndFeature, weightsPerFeature, weightsPerLabel, perLabelThetaNormalizer, alphaI); model.validate(); return model; } public static NaiveBayesModel materializeLocal(String modelfile) throws IOException { Vector weightsPerLabel = null; Vector perLabelThetaNormalizer = null; Vector weightsPerFeature = null; Matrix weightsPerLabelAndFeature; float alphaI; System.out.println(modelfile); ClassLoader loader = NaiveBayesModel.class.getClassLoader(); InputStream sin = loader.getResourceAsStream(modelfile); DataInputStream in = new DataInputStream(sin); try { alphaI = in.readFloat(); weightsPerFeature = VectorWritable.readVector(in); weightsPerLabel = new DenseVector(VectorWritable.readVector(in)); perLabelThetaNormalizer = new DenseVector(VectorWritable.readVector(in)); weightsPerLabelAndFeature = new SparseRowMatrix(weightsPerLabel.size(), weightsPerFeature.size()); for (int label = 0; label < weightsPerLabelAndFeature.numRows(); label++) { weightsPerLabelAndFeature.assignRow(label, VectorWritable.readVector(in)); } } finally { in.close(); } NaiveBayesModel model = new NaiveBayesModel(weightsPerLabelAndFeature, weightsPerFeature, weightsPerLabel, perLabelThetaNormalizer, alphaI); model.validate(); return model; } public void serialize(Path output, Configuration conf) throws IOException { FileSystem fs = output.getFileSystem(conf); FSDataOutputStream out = fs.create(new Path(output, "naiveBayesModel.bin")); try { out.writeFloat(alphaI); VectorWritable.writeVector(out, weightsPerFeature); VectorWritable.writeVector(out, weightsPerLabel); VectorWritable.writeVector(out, perlabelThetaNormalizer); for (int row = 0; row < weightsPerLabelAndFeature.numRows(); row++) { VectorWritable.writeVector(out, weightsPerLabelAndFeature.viewRow(row)); } } finally { Closeables.close(out, false); } } public void validate() { Preconditions.checkState(alphaI > 0, "alphaI has to be greater than 0!"); Preconditions.checkArgument(numFeatures > 0, "the vocab count has to be greater than 0!"); Preconditions.checkArgument(totalWeightSum > 0, "the totalWeightSum has to be greater than 0!"); Preconditions.checkNotNull(weightsPerLabel, "the number of labels has to be defined!"); Preconditions.checkArgument(weightsPerLabel.getNumNondefaultElements() > 0, "the number of labels has to be greater than 0!"); Preconditions.checkArgument(perlabelThetaNormalizer != null, "the theta normalizers have to be defined"); // Preconditions.checkArgument(perlabelThetaNormalizer.getNumNondefaultElements() // > 0, // "the number of theta normalizers has to be greater than 0!"); Preconditions.checkNotNull(weightsPerFeature, "the feature sums have to be defined"); Preconditions.checkArgument(weightsPerFeature.getNumNondefaultElements() > 0, "the feature sums have to be greater than 0!"); // Check if all thetas have same sign. /* * Iterator<Element> it = perlabelThetaNormalizer.iterateNonZero(); * while (it.hasNext()) { Element e = it.next(); * Preconditions.checkArgument(Math.signum(e.get()) == * Math.signum(minThetaNormalizer), e.get() + " " + * minThetaNormalizer); } */ } }