L1LossLearner.java
- package org.drip.learning.rxtor1;
- /*
- * -*- mode: java; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
- */
- /*!
- * Copyright (C) 2020 Lakshmi Krishnamurthy
- * Copyright (C) 2019 Lakshmi Krishnamurthy
- * Copyright (C) 2018 Lakshmi Krishnamurthy
- * Copyright (C) 2017 Lakshmi Krishnamurthy
- * Copyright (C) 2016 Lakshmi Krishnamurthy
- * Copyright (C) 2015 Lakshmi Krishnamurthy
- *
- * This file is part of DROP, an open-source library targeting analytics/risk, transaction cost analytics,
- * asset liability management analytics, capital, exposure, and margin analytics, valuation adjustment
- * analytics, and portfolio construction analytics within and across fixed income, credit, commodity,
- * equity, FX, and structured products. It also includes auxiliary libraries for algorithm support,
- * numerical analysis, numerical optimization, spline builder, model validation, statistical learning,
- * and computational support.
- *
- * https://lakshmidrip.github.io/DROP/
- *
- * DROP is composed of three modules:
- *
- * - DROP Product Core - https://lakshmidrip.github.io/DROP-Product-Core/
- * - DROP Portfolio Core - https://lakshmidrip.github.io/DROP-Portfolio-Core/
- * - DROP Computational Core - https://lakshmidrip.github.io/DROP-Computational-Core/
- *
- * DROP Product Core implements libraries for the following:
- * - Fixed Income Analytics
- * - Loan Analytics
- * - Transaction Cost Analytics
- *
- * DROP Portfolio Core implements libraries for the following:
- * - Asset Allocation Analytics
- * - Asset Liability Management Analytics
- * - Capital Estimation Analytics
- * - Exposure Analytics
- * - Margin Analytics
- * - XVA Analytics
- *
- * DROP Computational Core implements libraries for the following:
- * - Algorithm Support
- * - Computation Support
- * - Function Analysis
- * - Model Validation
- * - Numerical Analysis
- * - Numerical Optimizer
- * - Spline Builder
- * - Statistical Learning
- *
- * Documentation for DROP is Spread Over:
- *
- * - Main => https://lakshmidrip.github.io/DROP/
- * - Wiki => https://github.com/lakshmiDRIP/DROP/wiki
- * - GitHub => https://github.com/lakshmiDRIP/DROP
- * - Repo Layout Taxonomy => https://github.com/lakshmiDRIP/DROP/blob/master/Taxonomy.md
- * - Javadoc => https://lakshmidrip.github.io/DROP/Javadoc/index.html
- * - Technical Specifications => https://github.com/lakshmiDRIP/DROP/tree/master/Docs/Internal
- * - Release Versions => https://lakshmidrip.github.io/DROP/version.html
- * - Community Credits => https://lakshmidrip.github.io/DROP/credits.html
- * - Issues Catalog => https://github.com/lakshmiDRIP/DROP/issues
- * - JUnit => https://lakshmidrip.github.io/DROP/junit/index.html
- * - Jacoco => https://lakshmidrip.github.io/DROP/jacoco/index.html
- *
- * 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.
- */
- /**
- * <i>L1LossLearner</i> implements the Learner Class that holds the Space of Normed R<sup>x</sup> To Normed
- * R<sup>1</sup> Learning Functions that employs L<sub>1</sub> Empirical Loss Routine. Class-Specific
- * Asymptotic Sample, Covering Number based Upper Probability Bounds and other Parameters are also
- * maintained.
- *
- * <br><br>
- * The References are:
- *
- * <br><br>
- * <ul>
- * <li>
- * Alon, N., S. Ben-David, N. Cesa Bianchi, and D. Haussler (1997): Scale-sensitive Dimensions, Uniform
- * Convergence, and Learnability <i>Journal of Association of Computational Machinery</i> <b>44
- * (4)</b> 615-631
- * </li>
- * <li>
- * Anthony, M., and P. L. Bartlett (1999): <i>Artificial Neural Network Learning - Theoretical
- * Foundations</i> <b>Cambridge University Press</b> Cambridge, UK
- * </li>
- * <li>
- * Kearns, M. J., R. E. Schapire, and L. M. Sellie (1994): <i>Towards Efficient Agnostic Learning</i>
- * Machine Learning <b>17 (2)</b> 115-141
- * </li>
- * <li>
- * Lee, W. S., P. L. Bartlett, and R. C. Williamson (1998): The Importance of Convexity in Learning with
- * Squared Loss <i>IEEE Transactions on Information Theory</i> <b>44</b> 1974-1980
- * </li>
- * <li>
- * Vapnik, V. N. (1998): <i>Statistical learning Theory</i> <b>Wiley</b> New York
- * </li>
- * </ul>
- *
- * <br><br>
- * <ul>
- * <li><b>Module </b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ComputationalCore.md">Computational Core Module</a></li>
- * <li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/StatisticalLearningLibrary.md">Statistical Learning</a></li>
- * <li><b>Project</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/learning">Agnostic Learning Bounds under Empirical Loss Minimization Schemes</a></li>
- * <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/learning/rxtor1">Statistical Learning Empirical Loss Penalizer</a></li>
- * </ul>
- *
- * @author Lakshmi Krishnamurthy
- */
- public class L1LossLearner extends org.drip.learning.rxtor1.GeneralizedLearner {
- private org.drip.learning.bound.MeasureConcentrationExpectationBound _cleb = null;
- /**
- * L1LossLearner Constructor
- *
- * @param funcClassRxToR1 R^x To R^1 Function Class
- * @param cdpb The Covering Number based Deviation Upper Probability Bound Generator
- * @param regularizerFunc The Regularizer Function
- * @param cleb The Concentration of Measure based Loss Expectation Upper Bound Evaluator
- *
- * @throws java.lang.Exception Thrown if the Inputs are Invalid
- */
- public L1LossLearner (
- final org.drip.spaces.functionclass.NormedRxToNormedR1Finite funcClassRxToR1,
- final org.drip.learning.bound.CoveringNumberLossBound cdpb,
- final org.drip.learning.regularization.RegularizationFunction regularizerFunc,
- final org.drip.learning.bound.MeasureConcentrationExpectationBound cleb)
- throws java.lang.Exception
- {
- super (funcClassRxToR1, cdpb, regularizerFunc);
- if (null == (_cleb = cleb)) throw new java.lang.Exception ("L1LossLearner ctr: Invalid Inputs");
- }
- /**
- * Retrieve the Concentration of Measure based Loss Expectation Upper Bound Evaluator Instance
- *
- * @return The Concentration of Measure based Loss Expectation Upper Bound Evaluator Instance
- */
- public org.drip.learning.bound.MeasureConcentrationExpectationBound concentrationLossBoundEvaluator()
- {
- return _cleb;
- }
- @Override public double lossSampleCoveringNumber (
- final org.drip.spaces.instance.GeneralizedValidatedVector gvvi,
- final double dblEpsilon,
- final boolean bSupremum)
- throws java.lang.Exception
- {
- org.drip.spaces.functionclass.NormedRxToNormedR1Finite funcClassRxToR1 = functionClass();
- return bSupremum ? funcClassRxToR1.sampleSupremumCoveringNumber (gvvi, dblEpsilon) :
- funcClassRxToR1.sampleCoveringNumber (gvvi, dblEpsilon);
- }
- @Override public double empiricalLoss (
- final org.drip.function.definition.R1ToR1 funcLearnerR1ToR1,
- final org.drip.spaces.instance.GeneralizedValidatedVector gvviX,
- final org.drip.spaces.instance.GeneralizedValidatedVector gvviY)
- throws java.lang.Exception
- {
- if (null == funcLearnerR1ToR1 || null == gvviX || !(gvviX instanceof
- org.drip.spaces.instance.ValidatedR1) || null == gvviY || !(gvviY instanceof
- org.drip.spaces.instance.ValidatedR1))
- throw new java.lang.Exception ("L1LossLearner::empiricalLoss => Invalid Inputs");
- double[] adblX = ((org.drip.spaces.instance.ValidatedR1) gvviX).instance();
- double[] adblY = ((org.drip.spaces.instance.ValidatedR1) gvviY).instance();
- double dblEmpiricalLoss = 0.;
- int iNumSample = adblX.length;
- if (iNumSample != adblY.length)
- throw new java.lang.Exception ("L1LossLearner::empiricalLoss => Invalid Inputs");
- for (int i = 0; i < iNumSample; ++i)
- dblEmpiricalLoss += java.lang.Math.abs (funcLearnerR1ToR1.evaluate (adblX[i]) - adblY[i]);
- return dblEmpiricalLoss;
- }
- @Override public double empiricalLoss (
- final org.drip.function.definition.RdToR1 funcLearnerRdToR1,
- final org.drip.spaces.instance.GeneralizedValidatedVector gvviX,
- final org.drip.spaces.instance.GeneralizedValidatedVector gvviY)
- throws java.lang.Exception
- {
- if (null == funcLearnerRdToR1 || null == gvviX || !(gvviX instanceof
- org.drip.spaces.instance.ValidatedRd) || null == gvviY || !(gvviY instanceof
- org.drip.spaces.instance.ValidatedR1))
- throw new java.lang.Exception ("L1LossLearner::empiricalLoss => Invalid Inputs");
- double[][] aadblX = ((org.drip.spaces.instance.ValidatedRd) gvviX).instance();
- double[] adblY = ((org.drip.spaces.instance.ValidatedR1) gvviY).instance();
- double dblEmpiricalLoss = 0.;
- int iNumSample = aadblX.length;
- if (iNumSample != adblY.length)
- throw new java.lang.Exception ("L1LossLearner::empiricalLoss => Invalid Inputs");
- for (int i = 0; i < iNumSample; ++i)
- dblEmpiricalLoss += java.lang.Math.abs (funcLearnerRdToR1.evaluate (aadblX[i]) - adblY[i]);
- return dblEmpiricalLoss;
- }
- @Override public double empiricalRisk (
- final org.drip.measure.continuous.R1R1 distR1R1,
- final org.drip.function.definition.R1ToR1 funcLearnerR1ToR1,
- final org.drip.spaces.instance.GeneralizedValidatedVector gvviX,
- final org.drip.spaces.instance.GeneralizedValidatedVector gvviY)
- throws java.lang.Exception
- {
- if (null == distR1R1 || null == funcLearnerR1ToR1 || null == gvviX || !(gvviX instanceof
- org.drip.spaces.instance.ValidatedR1) || null == gvviY || !(gvviY instanceof
- org.drip.spaces.instance.ValidatedR1))
- throw new java.lang.Exception ("L1LossLearner::empiricalRisk => Invalid Inputs");
- double[] adblX = ((org.drip.spaces.instance.ValidatedR1) gvviX).instance();
- double[] adblY = ((org.drip.spaces.instance.ValidatedR1) gvviY).instance();
- double dblNormalizer = 0.;
- double dblEmpiricalLoss = 0.;
- int iNumSample = adblX.length;
- if (iNumSample != adblY.length)
- throw new java.lang.Exception ("L1LossLearner::empiricalRisk => Invalid Inputs");
- for (int i = 0; i < iNumSample; ++i) {
- double dblDensity = distR1R1.density (adblX[i], adblY[i]);
- dblNormalizer += dblDensity;
- dblEmpiricalLoss += dblDensity * java.lang.Math.abs (funcLearnerR1ToR1.evaluate (adblX[i]) -
- adblY[i]);
- }
- return dblEmpiricalLoss / dblNormalizer;
- }
- @Override public double empiricalRisk (
- final org.drip.measure.continuous.RdR1 distRdR1,
- final org.drip.function.definition.RdToR1 funcLearnerRdToR1,
- final org.drip.spaces.instance.GeneralizedValidatedVector gvviX,
- final org.drip.spaces.instance.GeneralizedValidatedVector gvviY)
- throws java.lang.Exception
- {
- if (null == distRdR1 || null == funcLearnerRdToR1 || null == gvviX || !(gvviX instanceof
- org.drip.spaces.instance.ValidatedRd) || null == gvviY || !(gvviY instanceof
- org.drip.spaces.instance.ValidatedR1))
- throw new java.lang.Exception ("L1LossLearner::empiricalRisk => Invalid Inputs");
- double[][] aadblX = ((org.drip.spaces.instance.ValidatedRd) gvviX).instance();
- double[] adblY = ((org.drip.spaces.instance.ValidatedR1) gvviY).instance();
- double dblNormalizer = 0.;
- double dblEmpiricalLoss = 0.;
- int iNumSample = aadblX.length;
- if (iNumSample != adblY.length)
- throw new java.lang.Exception ("L1LossLearner::empiricalRisk => Invalid Inputs");
- for (int i = 0; i < iNumSample; ++i) {
- double dblDensity = distRdR1.density (aadblX[i], adblY[i]);
- dblNormalizer += dblDensity;
- dblEmpiricalLoss += dblDensity * java.lang.Math.abs (funcLearnerRdToR1.evaluate (aadblX[i]) -
- adblY[i]);
- }
- return dblEmpiricalLoss / dblNormalizer;
- }
- }