TestStatisticAccumulator.java
package org.drip.validation.evidence;
/*
* -*- mode: java; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
*/
/*!
* Copyright (C) 2020 Lakshmi Krishnamurthy
* Copyright (C) 2019 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>TestStatisticAccumulator</i> contains the Instance Counts of the Sorted Test Statistic Values.
*
* <br><br>
* <ul>
* <li>
* Anfuso, F., D. Karyampas, and A. Nawroth (2017): A Sound Basel III Compliant Framework for
* Back-testing Credit Exposure Models
* https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2264620 <b>eSSRN</b>
* </li>
* <li>
* Diebold, F. X., T. A. Gunther, and A. S. Tay (1998): Evaluating Density Forecasts with
* Applications to Financial Risk Management, International Economic Review 39 (4) 863-883
* </li>
* <li>
* Kenyon, C., and R. Stamm (2012): Discounting, LIBOR, CVA, and Funding: Interest Rate and Credit
* Pricing, Palgrave Macmillan
* </li>
* <li>
* Wikipedia (2018): Probability Integral Transform
* https://en.wikipedia.org/wiki/Probability_integral_transform
* </li>
* <li>
* Wikipedia (2019): p-value https://en.wikipedia.org/wiki/P-value
* </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/ModelValidationAnalyticsLibrary.md">Model Validation Analytics Library</a></li>
* <li><b>Project</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/validation/README.md">Risk Factor and Hypothesis Validation, Evidence Processing, and Model Testing</a></li>
* <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/validation/evidence/README.md">Sample and Ensemble Evidence Processors</a></li>
* </ul>
* <br><br>
*
* @author Lakshmi Krishnamurthy
*/
public class TestStatisticAccumulator
{
private int _totalInstanceCount = 0;
private java.util.TreeMap<java.lang.Double, java.lang.Integer> _instanceCountMap = null;
/**
* Empty TestStatisticAccumulator Constructor
*/
public TestStatisticAccumulator()
{
}
/**
* Retrieve the Instance Counter Map
*
* @return The Instance Counter Map
*/
public java.util.Map<java.lang.Double, java.lang.Integer> instanceCountMap()
{
return _instanceCountMap;
}
/**
* Retrieve the Total Response Instances Count
*
* @return The Total Response Instances Count
*/
public int totalInstanceCount()
{
return _totalInstanceCount;
}
/**
* Add the specified Test Statistic Entry
*
* @param testStatistic The Test Statistic
*
* @return TRUE - The Test Statistic Entry successfully added
*/
public boolean addTestStatistic (
final double testStatistic)
{
if (!org.drip.numerical.common.NumberUtil.IsValid (testStatistic))
{
return false;
}
if (null == _instanceCountMap)
{
_instanceCountMap = new java.util.TreeMap<java.lang.Double, java.lang.Integer>();
}
_instanceCountMap.put (
testStatistic,
_instanceCountMap.containsKey (testStatistic) ? _instanceCountMap.get (testStatistic) + 1 : 1
);
++_totalInstanceCount;
return true;
}
/**
* Extract the Empirical Cumulative Test Statistic Probability from the Smallest Response Value
*
* @param testStatistic The Test Statistic
*
* @return The Empirical Cumulative Test Statistic Probability from the Smallest Response Value
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public double cumulativeProbabilityFromLeft (
final double testStatistic)
throws java.lang.Exception
{
if (!org.drip.numerical.common.NumberUtil.IsValid (testStatistic) || 0 == _totalInstanceCount)
{
throw new java.lang.Exception
("TestStatisticAccumulator::cumulativeProbabilityFromLeft => Invalid Inputs");
}
double cumulativeProbabilityFromLeft = 0;
for (double testStatisticKey : _instanceCountMap.keySet())
{
if (testStatisticKey > testStatistic)
{
break;
}
cumulativeProbabilityFromLeft += _instanceCountMap.get (testStatistic);
}
return cumulativeProbabilityFromLeft / _totalInstanceCount;
}
/**
* Extract the Empirical Cumulative Test Statistic Probability from the Largest Response Value
*
* @param testStatistic The Test Statistic
*
* @return The Empirical Cumulative Test Statistic Probability from the Largest Response Value
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public double cumulativeProbabilityFromRight (
final double testStatistic)
throws java.lang.Exception
{
if (!org.drip.numerical.common.NumberUtil.IsValid (testStatistic) || 0 == _totalInstanceCount)
{
throw new java.lang.Exception
("TestStatisticAccumulator::cumulativeProbabilityFromRight => Invalid Inputs");
}
double cumulativeProbabilityFromRight = 0;
for (double testStatisticKey : _instanceCountMap.descendingKeySet())
{
if (testStatisticKey < testStatistic)
{
break;
}
cumulativeProbabilityFromRight += _instanceCountMap.get (testStatistic);
}
return cumulativeProbabilityFromRight / _totalInstanceCount;
}
/**
* Perform a Probability Integral Transform to generate the Test Statistic CDF Distribution
*
* @return The Test Statistic CDF Distribution
*/
public org.drip.validation.hypothesis.ProbabilityIntegralTransform probabilityIntegralTransform()
{
int instanceCount = 0;
double totalInstanceCountReciprocal = 1. / _totalInstanceCount;
java.util.Map<java.lang.Double, java.lang.Double> testStatisticPValueMap = new
java.util.TreeMap<java.lang.Double, java.lang.Double>();
for (double testStatisticKey : _instanceCountMap.keySet())
{
instanceCount += _instanceCountMap.get (testStatisticKey);
testStatisticPValueMap.put (
testStatisticKey,
totalInstanceCountReciprocal * instanceCount
);
}
try
{
return new org.drip.validation.hypothesis.ProbabilityIntegralTransform (testStatisticPValueMap);
}
catch (java.lang.Exception e)
{
e.printStackTrace();
}
return null;
}
}