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pcaspersjenkins
pcaspers
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QPR-12188 move unit test
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‎QuantExt/test/CMakeLists.txt‎

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# cpp files, this list is maintained manually
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set(QuantExt-Test_SRC analyticcashsettledeuropeanengine.cpp
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set(QuantExt-Test_SRC ad.cpp
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analyticcashsettledeuropeanengine.cpp
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analyticlgmswaptionengine.cpp
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basecorrelationcurve.cpp
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bfrrvolsurface.cpp

‎QuantExt/test/ad.cpp‎

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/*
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Copyright (C) 2019 Quaternion Risk Management Ltd
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All rights reserved.
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This file is part of ORE, a free-software/open-source library
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for transparent pricing and risk analysis - http://opensourcerisk.org
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ORE is free software: you can redistribute it and/or modify it
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under the terms of the Modified BSD License. You should have received a
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copy of the license along with this program.
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The license is also available online at <http://opensourcerisk.org>
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This program is distributed on the basis that it will form a useful
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contribution to risk analytics and model standardisation, but WITHOUT
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ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
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FITNESS FOR A PARTICULAR PURPOSE. See the license for more details.
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*/
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#include <oret/toplevelfixture.hpp>
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#include <qle/ad/backwardderivatives.hpp>
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#include <qle/ad/forwardderivatives.hpp>
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#include <qle/ad/forwardevaluation.hpp>
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#include <qle/ad/ssaform.hpp>
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#include <qle/math/randomvariable_ops.hpp>
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#include <ql/math/distributions/normaldistribution.hpp>
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#include <ql/math/randomnumbers/inversecumulativerng.hpp>
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#include <ql/math/randomnumbers/mt19937uniformrng.hpp>
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#include <boost/test/unit_test.hpp>
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using namespace QuantExt;
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BOOST_FIXTURE_TEST_SUITE(QuantExtTestSuite, qle::test::TopLevelFixture)
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BOOST_AUTO_TEST_SUITE(AdTest)
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BOOST_AUTO_TEST_CASE(testForwardEvaluation) {
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constexpr Real tol = 1E-14;
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// z = x+y, z = ux = (x+y)x = x^2+yx
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ComputationGraph g;
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auto x = cg_var(g, "x", ComputationGraph::VarDoesntExist::Create);
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auto y = cg_var(g, "y", ComputationGraph::VarDoesntExist::Create);
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auto u = cg_add(g, x, y, "u");
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auto z = cg_mult(g, u, x, "z");
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BOOST_TEST_MESSAGE("SSA Form:\n" + ssaForm(g, getRandomVariableOpLabels()));
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std::vector<RandomVariable> values(g.size(), RandomVariable(1, 0.0));
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values[x] = RandomVariable(1, 2.0);
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values[y] = RandomVariable(1, 3.0);
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forwardEvaluation(g, values, getRandomVariableOps(1), RandomVariable::deleter, false);
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// BOOST_CHECK_CLOSE(values[x][0], 2.0, tol); // deleted
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// BOOST_CHECK_CLOSE(values[y][0], 3.0, tol);
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// BOOST_CHECK_CLOSE(values[u][0], 5.0, tol);
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BOOST_CHECK_CLOSE(values[z][0], 10.0, tol);
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}
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BOOST_AUTO_TEST_CASE(testBackwardDerivatives) {
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constexpr Real tol = 1E-14;
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// z = x+y, z = ux = (x+y)x = x^2+yx
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ComputationGraph g;
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auto x = cg_var(g, "x", ComputationGraph::VarDoesntExist::Create);
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auto y = cg_var(g, "y", ComputationGraph::VarDoesntExist::Create);
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auto u = cg_add(g, x, y, "u");
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auto z = cg_mult(g, u, x, "z");
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BOOST_TEST_MESSAGE("SSA Form:\n" + ssaForm(g, getRandomVariableOpLabels()));
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// forward evaluation
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std::vector<RandomVariable> values(g.size(), RandomVariable(1, 0.0));
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values[x] = RandomVariable(1, 2.0);
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values[y] = RandomVariable(1, 3.0);
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forwardEvaluation(g, values, getRandomVariableOps(1), RandomVariable::deleter, true,
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getRandomVariableOpNodeRequirements());
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// BOOST_CHECK_CLOSE(values[x][0], 2.0, tol); // deleted
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// BOOST_CHECK_CLOSE(values[y][0], 3.0, tol);
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// BOOST_CHECK_CLOSE(values[u][0], 5.0, tol);
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BOOST_CHECK_CLOSE(values[z][0], 10.0, tol);
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// backward derivatives
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std::vector<RandomVariable> derivativesBwd(g.size(), RandomVariable(1, 0.0));
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derivativesBwd[z] = RandomVariable(1, 1.0);
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std::vector<bool> keep(g.size(), false);
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keep[x] = true;
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keep[y] = true;
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keep[z] = true;
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keep[u] = true;
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backwardDerivatives(g, values, derivativesBwd, getRandomVariableGradients(1), RandomVariable::deleter, keep);
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// dz/dx = 2x+y
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BOOST_CHECK_CLOSE(derivativesBwd[x][0], 7.0, tol);
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// dz/dy = x
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BOOST_CHECK_CLOSE(derivativesBwd[y][0], 2.0, tol);
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// dz/du = x
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BOOST_CHECK_CLOSE(derivativesBwd[u][0], 2.0, tol);
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// dz/dz = 1
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BOOST_CHECK_CLOSE(derivativesBwd[z][0], 1.0, tol);
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}
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BOOST_AUTO_TEST_CASE(testForwardDerivatives) {
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constexpr Real tol = 1E-14;
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// z = x+y, z = ux = (x+y)x = x^2+yx
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ComputationGraph g;
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auto x = cg_var(g, "x", ComputationGraph::VarDoesntExist::Create);
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auto y = cg_var(g, "y", ComputationGraph::VarDoesntExist::Create);
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auto u = cg_add(g, x, y, "u");
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auto z = cg_mult(g, u, x, "z");
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BOOST_TEST_MESSAGE("SSA Form:\n" + ssaForm(g, getRandomVariableOpLabels()));
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// forward evaluation
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std::vector<RandomVariable> values(g.size(), RandomVariable(1, 0.0));
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values[x] = RandomVariable(1, 2.0);
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values[y] = RandomVariable(1, 3.0);
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forwardEvaluation(g, values, getRandomVariableOps(1), RandomVariable::deleter, true,
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getRandomVariableOpNodeRequirements());
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// forward derivatives
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std::vector<RandomVariable> derivativesFwdX(g.size(), RandomVariable(1, 0.0));
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std::vector<RandomVariable> derivativesFwdY(g.size(), RandomVariable(1, 0.0));
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derivativesFwdX[x] = RandomVariable(1, 1.0);
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derivativesFwdY[y] = RandomVariable(1, 1.0);
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forwardDerivatives(g, values, derivativesFwdX, getRandomVariableGradients(1), RandomVariable::deleter);
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forwardDerivatives(g, values, derivativesFwdY, getRandomVariableGradients(1), RandomVariable::deleter);
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// dz/dx = 2x+y
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BOOST_CHECK_CLOSE(derivativesFwdX[z][0], 7.0, tol);
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// dz/dy = x
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BOOST_CHECK_CLOSE(derivativesFwdY[z][0], 2.0, tol);
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}
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BOOST_AUTO_TEST_CASE(testIndicatorDerivative) {
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BOOST_TEST_MESSAGE("Testing indicator derivative...");
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Size n = 5000000; // number of samples
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Real epsilon = 0.05; // indcator derivative bandwidth
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ComputationGraph g;
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auto z = cg_var(g, "z", ComputationGraph::VarDoesntExist::Create); // z ~ N(z0,1)
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auto y = cg_indicatorGt(g, z, cg_const(g, 0.0)); // ind = 1_{z>0}
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InverseCumulativeRng<MersenneTwisterUniformRng, InverseCumulativeNormal> normal(MersenneTwisterUniformRng(42));
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Real tol = 30.0E-4;
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Real z0 = -3.0;
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while (z0 < 3.01) {
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std::vector<RandomVariable> values(g.size(), RandomVariable(n)), derivatives(g.size(), RandomVariable(n));
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BOOST_TEST_MESSAGE("z0=" << z0 << ":");
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for (Size i = 0; i < n; ++i) {
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values[z].set(i, z0 + normal.next().value);
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}
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forwardEvaluation(g, values, getRandomVariableOps(n));
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Real av = expectation(values[y])[0];
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Real refAv = 1.0 - CumulativeNormalDistribution()(-z0);
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BOOST_TEST_MESSAGE("E( 1_{z>0} ) = " << av << ", reference " << refAv << ", diff " << refAv - av);
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BOOST_CHECK_SMALL(av - refAv, tol);
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derivatives[y] = RandomVariable(n, 1.0);
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backwardDerivatives(g, values, derivatives,
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getRandomVariableGradients(1, 2, QuantLib::LsmBasisSystem::Monomial, epsilon));
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Real dav = expectation(derivatives[z])[0];
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Real refDav = NormalDistribution()(-z0);
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// it is dz / dz0 = 1, so we are really computing d/dz0 E(1_{z>0})
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// and we have E(1_{z>0}) = 1 - \Phi(-z0), so d/dz0 E(1_{z>0}) = phi(-z0)
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BOOST_TEST_MESSAGE("E( d/dz 1_{z>0} ) = " << dav << ", reference " << refDav << ", diff " << refDav - dav);
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BOOST_CHECK_SMALL(dav - refDav, tol);
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z0 += 0.5;
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}
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}
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BOOST_AUTO_TEST_SUITE_END()
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BOOST_AUTO_TEST_SUITE_END()

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