org.apache.mahout.classifier.sgd.OnlineLogisticRegression.java Source code

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Here is the source code for org.apache.mahout.classifier.sgd.OnlineLogisticRegression.java

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/*
 * 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.sgd;

import org.apache.hadoop.io.Writable;
import org.apache.mahout.math.DenseMatrix;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.MatrixWritable;
import org.apache.mahout.math.VectorWritable;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

/**
 * Extends the basic on-line logistic regression learner with a specific set of learning
 * rate annealing schedules.
 */
public class OnlineLogisticRegression extends AbstractOnlineLogisticRegression implements Writable {
    public static final int WRITABLE_VERSION = 1;

    // these next two control decayFactor^steps exponential type of annealing
    // learning rate and decay factor
    private double mu0 = 1;
    private double decayFactor = 1 - 1.0e-3;

    // these next two control 1/steps^forget type annealing
    private int stepOffset = 10;
    // -1 equals even weighting of all examples, 0 means only use exponential annealing
    private double forgettingExponent = -0.5;

    // controls how per term annealing works
    private int perTermAnnealingOffset = 20;

    public OnlineLogisticRegression() {
        // private constructor available for serialization, but not normal use
    }

    public OnlineLogisticRegression(int numCategories, int numFeatures, PriorFunction prior) {
        this.numCategories = numCategories;
        this.prior = prior;

        updateSteps = new DenseVector(numFeatures);
        updateCounts = new DenseVector(numFeatures).assign(perTermAnnealingOffset);
        beta = new DenseMatrix(numCategories - 1, numFeatures);
    }

    /**
     * Chainable configuration option.
     *
     * @param alpha New value of decayFactor, the exponential decay rate for the learning rate.
     * @return This, so other configurations can be chained.
     */
    public OnlineLogisticRegression alpha(double alpha) {
        this.decayFactor = alpha;
        return this;
    }

    @Override
    public OnlineLogisticRegression lambda(double lambda) {
        // we only over-ride this to provide a more restrictive return type
        super.lambda(lambda);
        return this;
    }

    /**
     * Chainable configuration option.
     *
     * @param learningRate New value of initial learning rate.
     * @return This, so other configurations can be chained.
     */
    public OnlineLogisticRegression learningRate(double learningRate) {
        this.mu0 = learningRate;
        return this;
    }

    public OnlineLogisticRegression stepOffset(int stepOffset) {
        this.stepOffset = stepOffset;
        return this;
    }

    public OnlineLogisticRegression decayExponent(double decayExponent) {
        if (decayExponent > 0) {
            decayExponent = -decayExponent;
        }
        this.forgettingExponent = decayExponent;
        return this;
    }

    @Override
    public double perTermLearningRate(int j) {
        return Math.sqrt(perTermAnnealingOffset / updateCounts.get(j));
    }

    @Override
    public double currentLearningRate() {
        return mu0 * Math.pow(decayFactor, getStep()) * Math.pow(getStep() + stepOffset, forgettingExponent);
    }

    public void copyFrom(OnlineLogisticRegression other) {
        super.copyFrom(other);
        mu0 = other.mu0;
        decayFactor = other.decayFactor;

        stepOffset = other.stepOffset;
        forgettingExponent = other.forgettingExponent;

        perTermAnnealingOffset = other.perTermAnnealingOffset;
    }

    public OnlineLogisticRegression copy() {
        close();
        OnlineLogisticRegression r = new OnlineLogisticRegression(numCategories(), numFeatures(), prior);
        r.copyFrom(this);
        return r;
    }

    @Override
    public void write(DataOutput out) throws IOException {
        out.writeInt(WRITABLE_VERSION);
        out.writeDouble(mu0);
        out.writeDouble(getLambda());
        out.writeDouble(decayFactor);
        out.writeInt(stepOffset);
        out.writeInt(step);
        out.writeDouble(forgettingExponent);
        out.writeInt(perTermAnnealingOffset);
        out.writeInt(numCategories);
        MatrixWritable.writeMatrix(out, beta);
        PolymorphicWritable.write(out, prior);
        VectorWritable.writeVector(out, updateCounts);
        VectorWritable.writeVector(out, updateSteps);
    }

    @Override
    public void readFields(DataInput in) throws IOException {
        int version = in.readInt();
        if (version == WRITABLE_VERSION) {
            mu0 = in.readDouble();
            lambda(in.readDouble());
            decayFactor = in.readDouble();
            stepOffset = in.readInt();
            step = in.readInt();
            forgettingExponent = in.readDouble();
            perTermAnnealingOffset = in.readInt();
            numCategories = in.readInt();
            beta = MatrixWritable.readMatrix(in);
            prior = PolymorphicWritable.read(in, PriorFunction.class);

            updateCounts = VectorWritable.readVector(in);
            updateSteps = VectorWritable.readVector(in);
        } else {
            throw new IOException("Incorrect object version, wanted " + WRITABLE_VERSION + " got " + version);
        }
    }
}