Java tutorial
/** * 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 org.apache.mahout.classifier.naivebayes; import java.io.IOException; 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; /** 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 Vector weightsPerFeature; private final Matrix weightsPerLabelAndFeature; private final float alphaI; private final double numFeatures; private final double totalWeightSum; private final boolean isComplementary; public final static String COMPLEMENTARY_MODEL = "COMPLEMENTARY_MODEL"; public NaiveBayesModel(Matrix weightMatrix, Vector weightsPerFeature, Vector weightsPerLabel, Vector thetaNormalizer, float alphaI, boolean isComplementary) { this.weightsPerLabelAndFeature = weightMatrix; this.weightsPerFeature = weightsPerFeature; this.weightsPerLabel = weightsPerLabel; this.perlabelThetaNormalizer = thetaNormalizer; this.numFeatures = weightsPerFeature.getNumNondefaultElements(); this.totalWeightSum = weightsPerLabel.zSum(); this.alphaI = alphaI; this.isComplementary = isComplementary; } public double labelWeight(int label) { return weightsPerLabel.getQuick(label); } public double thetaNormalizer(int label) { return perlabelThetaNormalizer.get(label); } 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 boolean isComplemtary() { return isComplementary; } public static NaiveBayesModel materialize(Path output, Configuration conf) throws IOException { FileSystem fs = output.getFileSystem(conf); Vector weightsPerLabel; Vector perLabelThetaNormalizer = null; Vector weightsPerFeature; Matrix weightsPerLabelAndFeature; float alphaI; boolean isComplementary; try (FSDataInputStream in = fs.open(new Path(output, "naiveBayesModel.bin"))) { alphaI = in.readFloat(); isComplementary = in.readBoolean(); weightsPerFeature = VectorWritable.readVector(in); weightsPerLabel = new DenseVector(VectorWritable.readVector(in)); if (isComplementary) { 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)); } } NaiveBayesModel model = new NaiveBayesModel(weightsPerLabelAndFeature, weightsPerFeature, weightsPerLabel, perLabelThetaNormalizer, alphaI, isComplementary); model.validate(); return model; } public void serialize(Path output, Configuration conf) throws IOException { FileSystem fs = output.getFileSystem(conf); try (FSDataOutputStream out = fs.create(new Path(output, "naiveBayesModel.bin"))) { out.writeFloat(alphaI); out.writeBoolean(isComplementary); VectorWritable.writeVector(out, weightsPerFeature); VectorWritable.writeVector(out, weightsPerLabel); if (isComplementary) { VectorWritable.writeVector(out, perlabelThetaNormalizer); } for (int row = 0; row < weightsPerLabelAndFeature.numRows(); row++) { VectorWritable.writeVector(out, weightsPerLabelAndFeature.viewRow(row)); } } } 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.checkNotNull(weightsPerFeature, "the feature sums have to be defined"); Preconditions.checkArgument(weightsPerFeature.getNumNondefaultElements() > 0, "the feature sums have to be greater than 0!"); if (isComplementary) { 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.checkArgument( Math.signum(perlabelThetaNormalizer.minValue()) == Math .signum(perlabelThetaNormalizer.maxValue()), "Theta normalizers do not all have the same sign"); Preconditions.checkArgument( perlabelThetaNormalizer.getNumNonZeroElements() == perlabelThetaNormalizer.size(), "Theta normalizers can not have zero value."); } } }