java实现随机森林RandomForest的示例代码

java实现随机森林RandomForest的示例代码

随机森林是由多棵树组成的分类或回归方法。主要思想来源于Bagging算法，Bagging技术思想主要是给定一弱分类器及训练集，让该学习算法训练多轮，每轮的训练集由原始训练集中有放回的随机抽取，大小一般跟原始训练集相当，这样依次训练多个弱分类器，最终的分类由这些弱分类器组合，对于分类问题一般采用多数投票法，对于回归问题一般采用简单平均法。随机森林在bagging的基础上，每个弱分类器都是决策树，决策树的生成过程中中，在属性的选择上增加了依一定概率选择属性，在这些属性中选择最佳属性及分割点，传统做法一般是全部属性中去选择最佳属性，这样随机森林有了样本选择的随机性，属性选择的随机性，这样一来增加了每个分类器的差异性、不稳定性及一定程度上避免每个分类器的过拟合（一般决策树有过拟合现象），由此组合分类器增加了最终的泛化能力。下面是代码的简单实现

/**

* 随机森林 回归问题

* @author ysh 1208706282

*

*/

publicvBQJwK class RandomForest {

List mSamples;

List mCarts;

double mFeatureRate;

int mMaxDepth;

int mMinLeaf;

Random mRandom;

/**

* 加载数据 回归树

* @param path

* @param regex

* @throws Exception

*/

public void loadData(String path,String regex) throws Exception{

mSamples = new ArrayList();

BufferedReader reader = new BufferedReaderhttp://(new FileReader(path));

String line = null;

String splits[] = null;

Sample sample = null;

while(null != (line=reader.readLine())){

splits = line.split(regex);

sample = new Sample();

sample.label = Double.valueOf(splits[0]);

sample.feature = new ArrayList(splits.length-1);

for(int i=0;i

sample.feature.add(new Double(splits[i+1]));

}

mSamples.add(sample);

}

reader.close();

}

public void train(int iters){

mCarts = new ArrayList(iters);

Cart cart = null;

for(int iter=0;iter

cart = new Cart();

cart.mFeatureRate = mFeatureRate;

cart.mMaxDepth = mMaxDepth;

cart.mMinLeaf = mMinLeaf;

cart.mRandom = mRandom;

List s = new ArrayList(mSamples.size());

for(int i=0;i

s.add(mSamples.get(cart.mRandom.nextInt(mSamples.size())));

}

cart.setData(s);

cart.train();

mCarts.add(cart);

System.out.println("iter: "+iter);

s = null;

}

}

/**

* 回归问题简单平均法 分类问题多数投票法

* @param sample

* @return

*/

public double classify(Sample sample){

double val = 0;

for(Cart cart:mCarts){

val += cart.classify(sample);

}

return val/mCarts.size();

}

/**

* @param args

* @throws Exception

*/

public static void main(String[] args) throws Exception {

// TODO Auto-generated method stub

RandomForest forest = new RandomForest();

forest.loadData("F:/2016-contest/20161001/train_data_1.csv", ",");

forest.mFeatureRate = 0.8;

forest.mMaxDepth = 3;

forest.mMinLeaf = 1;

forest.mRandom = new Random();

forest.mRandom.setSeed(100);

forest.train(100);

List samples = Cart.loadTestData("F:/2016-contest/20161001/valid_data_1.csv", true, ",");

double sum = 0;

for(Sample s:samples){

double val = forest.classify(s);

sum += (val-s.label)*(val-s.label);

System.out.println(val+" "+s.label);

}

System.out.println(sum/samples.size()+" "+sum);

System.out.println(System.currentTimeMillis());

}

}

sample.feature.add(new Double(splits[i+1]));

}

mSamples.add(sample);

}

reader.close();

}

public void train(int iters){

mCarts = new ArrayList(iters);

Cart cart = null;

for(int iter=0;iter

cart = new Cart();

cart.mFeatureRate = mFeatureRate;

cart.mMaxDepth = mMaxDepth;

cart.mMinLeaf = mMinLeaf;

cart.mRandom = mRandom;

List s = new ArrayList(mSamples.size());

for(int i=0;i

s.add(mSamples.get(cart.mRandom.nextInt(mSamples.size())));

}

cart.setData(s);

cart.train();

mCarts.add(cart);

System.out.println("iter: "+iter);

s = null;

}

}

/**

* 回归问题简单平均法 分类问题多数投票法

* @param sample

* @return

*/

public double classify(Sample sample){

double val = 0;

for(Cart cart:mCarts){

val += cart.classify(sample);

}

return val/mCarts.size();

}

/**

* @param args

* @throws Exception

*/

public static void main(String[] args) throws Exception {

// TODO Auto-generated method stub

RandomForest forest = new RandomForest();

forest.loadData("F:/2016-contest/20161001/train_data_1.csv", ",");

forest.mFeatureRate = 0.8;

forest.mMaxDepth = 3;

forest.mMinLeaf = 1;

forest.mRandom = new Random();

forest.mRandom.setSeed(100);

forest.train(100);

List samples = Cart.loadTestData("F:/2016-contest/20161001/valid_data_1.csv", true, ",");

double sum = 0;

for(Sample s:samples){

double val = forest.classify(s);

sum += (val-s.label)*(val-s.label);

System.out.println(val+" "+s.label);

}

System.out.println(sum/samples.size()+" "+sum);

System.out.println(System.currentTimeMillis());

}

}

cart = new Cart();

cart.mFeatureRate = mFeatureRate;

cart.mMaxDepth = mMaxDepth;

cart.mMinLeaf = mMinLeaf;

cart.mRandom = mRandom;

List s = new ArrayList(mSamples.size());

for(int i=0;i

s.add(mSamples.get(cart.mRandom.nextInt(mSamples.size())));

}

cart.setData(s);

cart.train();

mCarts.add(cart);

System.out.println("iter: "+iter);

s = null;

}

}

/**

* 回归问题简单平均法 分类问题多数投票法

* @param sample

* @return

*/

public double classify(Sample sample){

double val = 0;

for(Cart cart:mCarts){

val += cart.classify(sample);

}

return val/mCarts.size();

}

/**

* @param args

* @throws Exception

*/

public static void main(String[] args) throws Exception {

// TODO Auto-generated method stub

RandomForest forest = new RandomForest();

forest.loadData("F:/2016-contest/20161001/train_data_1.csv", ",");

forest.mFeatureRate = 0.8;

forest.mMaxDepth = 3;

forest.mMinLeaf = 1;

forest.mRandom = new Random();

forest.mRandom.setSeed(100);

forest.train(100);

List samples = Cart.loadTestData("F:/2016-contest/20161001/valid_data_1.csv", true, ",");

double sum = 0;

for(Sample s:samples){

double val = forest.classify(s);

sum += (val-s.label)*(val-s.label);

System.out.println(val+" "+s.label);

}

System.out.println(sum/samples.size()+" "+sum);

System.out.println(System.currentTimeMillis());

}

}

s.add(mSamples.get(cart.mRandom.nextInt(mSamples.size())));

}

cart.setData(s);

cart.train();

mCarts.add(cart);

System.out.println("iter: "+iter);

s = null;

}

}

/**

* 回归问题简单平均法 分类问题多数投票法

* @param sample

* @return

*/

public double classify(Sample sample){

double val = 0;

for(Cart cart:mCarts){

val += cart.classify(sample);

}

return val/mCarts.size();

}

/**

* @param args

* @throws Exception

*/

public static void main(String[] args) throws Exception {

// TODO Auto-generated method stub

RandomForest forest = new RandomForest();

forest.loadData("F:/2016-contest/20161001/train_data_1.csv", ",");

forest.mFeatureRate = 0.8;

forest.mMaxDepth = 3;

forest.mMinLeaf = 1;

forest.mRandom = new Random();

forest.mRandom.setSeed(100);

forest.train(100);

List samples = Cart.loadTestData("F:/2016-contest/20161001/valid_data_1.csv", true, ",");

double sum = 0;

for(Sample s:samples){

double val = forest.classify(s);

sum += (val-s.label)*(val-s.label);

System.out.println(val+" "+s.label);

}

System.out.println(sum/samples.size()+" "+sum);

System.out.println(System.currentTimeMillis());

}

}

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