/*
* Copyright [2013-2014] eBay Software Foundation
*
* Licensed 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 ml.shifu.shifu.core.dtrain.lr;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import ml.shifu.guagua.io.Combinable;
import ml.shifu.guagua.io.HaltBytable;
/**
* A model class to store logistic regression weight on first iteration by using {@link #parameters}, while in other
* iterations {@link #parameters} is used to store gradients.
*
* <p>
* To make all workers started at the same model, master will compute a consistent model weights at the first iteration
* and then send to all the workers. Workers will start computing from the second iteration.
*
* <p>
* Workers are responsible to compute local accumulated gradients and send to master while master accumulates all
* gradients together to build a global model.
*/
public class LogisticRegressionParams extends HaltBytable implements Combinable<LogisticRegressionParams>{
/**
* Model weights in the first iteration, gradients in other iterations.
*/
private double[] parameters;
/**
* Current test error which can be sent to master
*/
private double testError = 0;
/**
* Current train error which can be sent to master
*/
private double trainError = 0;
/**
* Training record count in one worker
*/
private long trainSize;
/**
* Testing record count in one worker
*/
private long testSize;
public LogisticRegressionParams() {
}
public LogisticRegressionParams(double[] parameters) {
this.parameters = parameters;
}
public LogisticRegressionParams(double[] parameters, double trainError, double testError, long trainSize,
long testSize) {
this.parameters = parameters;
this.trainError = trainError;
this.testError = testError;
this.trainSize = trainSize;
this.testSize = testSize;
}
public double[] getParameters() {
return parameters;
}
public void setParameters(double[] parameters) {
this.parameters = parameters;
}
/**
* @return the trainSize
*/
public long getTrainSize() {
return trainSize;
}
/**
* @param trainSize
* the trainSize to set
*/
public void setTrainSize(long trainSize) {
this.trainSize = trainSize;
}
@Override
public LogisticRegressionParams combine(LogisticRegressionParams from) {
assert from != null;
this.trainError += from.trainError;
this.testError += from.testError;
this.trainSize+=from.trainSize;
this.testSize+=from.testSize;
assert this.parameters != null && from.parameters != null;
for(int i = 0; i < this.parameters.length; i++) {
this.parameters[i] += from.parameters[i];
}
return this;
}
@Override
public void doWrite(DataOutput out) throws IOException {
if(parameters == null) {
out.writeInt(0);
} else {
out.writeInt(this.parameters.length);
for(int i = 0; i < this.parameters.length; i++) {
out.writeDouble(this.parameters[i]);
}
}
out.writeDouble(this.trainError);
out.writeDouble(this.testError);
out.writeLong(this.trainSize);
out.writeLong(this.testSize);
}
@Override
public void doReadFields(DataInput in) throws IOException {
int length = in.readInt();
this.parameters = new double[length];
for(int i = 0; i < length; i++) {
this.parameters[i] = in.readDouble();
}
this.trainError = in.readDouble();
this.testError = in.readDouble();
this.trainSize = in.readLong();
this.testSize = in.readLong();
}
/**
* @return the testError
*/
public double getTestError() {
return testError;
}
/**
* @param testError
* the testError to set
*/
public void setTestError(double testError) {
this.testError = testError;
}
/**
* @return the trainError
*/
public double getTrainError() {
return trainError;
}
/**
* @param trainError
* the trainError to set
*/
public void setTrainError(double trainError) {
this.trainError = trainError;
}
/**
* @return the testSize
*/
public long getTestSize() {
return testSize;
}
/**
* @param testSize
* the testSize to set
*/
public void setTestSize(long testSize) {
this.testSize = testSize;
}
}