/**
* 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;
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 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);
}*/
}
}