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Conformal AI package, including all data IO, transformations, machine learning models and predictor classes. Without inclusion of chemistry-dependent code.
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/*
 * Copyright (C) Aros Bio AB.
 *
 * CPSign is an Open Source Software that is dual licensed to allow you to choose a license that best suits your requirements:
 *
 * 1) GPLv3 (GNU General Public License Version 3) with Additional Terms, including an attribution clause as well as a limitation to use the software for commercial purposes.
 *
 * 2) CPSign Proprietary License that allows you to use CPSign for commercial activities, such as in a revenue-generating operation or environment, or integrate CPSign in your proprietary software without worrying about disclosing the source code of your proprietary software, which is required if you choose to use the software under GPLv3 license. See arosbio.com/cpsign/commercial-license for details.
 */
package com.arosbio.ml.algorithms.svm;

import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;

import com.arosbio.commons.GlobalConfig;
import com.arosbio.data.DataRecord;
import com.arosbio.data.FeatureVector;
import com.arosbio.ml.algorithms.impl.DefaultMLParameterSettings;
import com.arosbio.ml.algorithms.impl.LibSvm;
import com.arosbio.ml.algorithms.impl.LibSvm.KernelType;
import com.arosbio.ml.algorithms.impl.LibSvm.SvmType;

import libsvm.svm_model;
import libsvm.svm_parameter;

public class EpsilonSVR implements SVR {

	public static final String ALG_NAME = "EpsilonSVR";
	public static final int ALG_ID = 2;

	/**
	 * Parameters that holds all info
	 */
	private svm_parameter parameters = LibSvm.getDefaultParams(SvmType.EPSILON_SVR);
	private svm_model svm;
	private long seed = GlobalConfig.getInstance().getRNGSeed();


	// Cost
	public double getC() {
		return parameters.C;
	}

	public void setC(double cost) {
		parameters.C=cost;
	}

	public EpsilonSVR withC(double cost){
		parameters.C = cost;
		return this;
	}

	// Epsilon
	public double getEpsilon() {
		return parameters.eps;
	}

	public void setEpsilon(double eps) {
		parameters.eps = eps;
	}

	public EpsilonSVR withEpsilon(double eps){
		parameters.eps = eps;
		return this;
	}

	// SVR epsilon
	public double getSVREpsilon() {
		return parameters.p;
	}

	public void setSVREpsilon(double eps) {
		parameters.p = eps;
	}

	public EpsilonSVR withSVREpsilon(double eps) {
		parameters.p = eps;
		return this;
	}

	// Kernel type
	public KernelType getKernel() {
		return KernelType.forID(parameters.kernel_type);
	}

	public void setKernel(KernelType kernel) {
		parameters.kernel_type = kernel.id;
	}

	public EpsilonSVR withKernel(KernelType kernel) {
		parameters.kernel_type = kernel.id;
		return this;
	}

	/// Gamma
	public double getGamma() {
		return parameters.gamma;
	}
	
	public void setGamma(double gamma){
		if (gamma < 0)
			throw new IllegalArgumentException("Parameter 'gamma' must be >=0");
		parameters.gamma = gamma;
	}

	public EpsilonSVR withGamma(double gamma){
		setGamma(gamma);
		return this;
	}
	
	// KERNEL DEGREE
	public int getDegree() {
		return parameters.degree;
	}

	public void setDegree(int degree) {
		parameters.degree = degree;
	}

	public EpsilonSVR withDegree(int degree) {
		parameters.degree = degree;
		return this;
	}

	// KERNEL COEF0
	public double getCoef0() {
		return parameters.coef0;
	}

	public void setCoef0(double coef0) {
		parameters.coef0=coef0;
	}

	public EpsilonSVR withCoef0(double coef0) {
		parameters.coef0=coef0;
		return this;
	}

	// CACHE SIZE
	public double getCacheSize() {
		return parameters.cache_size;
	}

	public void setCacheSize(double cacheMB) {
		if (cacheMB < 100)
			throw new IllegalArgumentException("Parameter 'cache-size' must be >=100");
		parameters.cache_size = cacheMB;
	}

	public EpsilonSVR withCacheSize(double cacheMB) {
		setCacheSize(cacheMB);
		return this;
	}

	// SHRINKING
	public boolean getShrinking() {
		return parameters.shrinking == 0 ? false : true;
	}

	public void setShrinking(boolean doShrinking) {
		parameters.shrinking = (doShrinking? 1 : 0);
	}

	public EpsilonSVR withShrinking(boolean doShrinking){
		setShrinking(doShrinking);
		return this;
	}


	public List getConfigParameters(){
		List params = new ArrayList<>();
		params.add(DefaultMLParameterSettings.COST_CONFIG);
		params.add(DefaultMLParameterSettings.SVR_EPSILON_CONFIG);
		params.addAll(LibSvm.GENERAL_CONFIG_PARAMS);
		return params;
	}
	
	@Override
	public void setConfigParameters(Map params) throws IllegalStateException, IllegalArgumentException {
		svm_parameter clone = (svm_parameter)parameters.clone();
		LibSvm.setConfigParameters(clone, params);
		parameters = clone;
	}

	@Override
	public EpsilonSVR clone() {
		EpsilonSVR clone = new EpsilonSVR();
		// Only copy the actual parameters 
		clone.parameters = (svm_parameter) parameters.clone();
		clone.seed = seed;
		return clone;
	}

	@Override
	public String getName() {
		return ALG_NAME;
	}

	@Override
	public int getID() {
		return ALG_ID;
	}
	
	@Override
	public String getDescription() {
		return "Support Vector Regression (SVR) implemented in LIBSVM.";
	}

	@Override
	public Map getProperties() {
		Map prop = LibSvm.toProperties(parameters);
		prop.put(ML_NAME_PARAM_KEY, ALG_NAME);
		prop.put(ML_ID_PARAM_KEY, ALG_ID);
		return prop;
	}

	@Override
	public void setSeed(long seed) {
		this.seed = seed;
	}

	@Override
	public Long getSeed() {
		return seed;
	}

	@Override
	public boolean isFitted() {
		return svm!=null;
	}
	
	@Override
	public String toString() {
		return ALG_NAME;
	}

	/* 
	 * =================================================
	 * 			TRAIN
	 * =================================================
	 */

	@Override
	public void train(List trainingset) throws IllegalArgumentException {
		svm = LibSvm.train(parameters, trainingset, seed);
	}

	@Override
	public void fit(List trainingset) throws IllegalArgumentException {
		svm = LibSvm.train(parameters, trainingset, seed);
	}

	/* 
	 * =================================================
	 * 			PREDICTIONS
	 * =================================================
	 */

	@Override
	public double predictValue(FeatureVector example) throws IllegalStateException {
		return LibSvm.predictValue(svm, example);
	}

	@Override
	public Map predictDistanceToHyperplane(FeatureVector example) throws IllegalStateException {
		return LibSvm.predictDistanceToHyperplane(svm, example);
	}

	/* 
	 * =================================================
	 * 			I/O
	 * =================================================
	 */

	@Override
	public void saveToStream(OutputStream ostream) throws IOException, IllegalStateException {
		LibSvm.saveToStream(svm, ostream);
	}

	@Override
	public void loadFromStream(InputStream istream) throws IOException {
		svm = LibSvm.loadFromStream(istream);
		parameters = svm.param;
	}

}