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added tests for LinearSVM with some Optimizers

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This commit is contained in:
Ayush
2019-04-07 22:05:07 -04:00
committed by Ryan Curtin
parent 8ae871d7ad
commit ac40c72312
2 changed files with 147 additions and 33 deletions
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src/mlpack/tests/CMakeLists.txt
+2 -1
View File
@@ -184,7 +184,8 @@ set(parallel_tests
"SVDIncrementalTest;SVDBatchTest;"
"LocalCoordinateCodingTest;FeedForwardNetworkTest;SparseAutoencoderTest;"
"GMMTest;CFTest;ConvolutionalNetworkTest;HMMTest;LARSTest;"
"LogisticRegressionTest")
"LogisticRegressionTest"
"LinearSVMTest")
# Add tests to the testing framework
# Get the list of sources from the test target
src/mlpack/tests/linear_svm_test.cpp
+145 -32
View File
@@ -439,31 +439,6 @@ BOOST_AUTO_TEST_CASE(LinearSVMFunctionSeparableGradient)
}
}
/**
* Test training of linear svm on a simple dataset using
* Parallel SGD optimizer.
*/
BOOST_AUTO_TEST_CASE(LinearSVMPSGDSimpleTest)
{
// A very simple fake dataset
arma::mat dataset = "2 0 0;"
"0 0 0;"
"0 2 1;"
"1 0 2;"
"0 1 0";
// Corresponding labels
arma::Row<size_t> labels = "1 0 1";
// Create a linear svm object using a custom Parallel
// SGD object.
ens::ParallelSGD<> psgd(1000, 3, 1e-5);
LinearSVM<arma::mat> lsvm(dataset, labels, 2, 0.0001, psgd);
// Compare training accuracy to 100.
const double acc = lsvm.ComputeAccuracy(dataset, labels);
BOOST_REQUIRE_CLOSE(acc, 100.0, 0.5);
}
/**
* Test training of linear svm on a simple dataset using
@@ -471,6 +446,9 @@ BOOST_AUTO_TEST_CASE(LinearSVMPSGDSimpleTest)
*/
BOOST_AUTO_TEST_CASE(LinearSVMLGFGSSimpleTest)
{
const size_t numClasses = 2;
const double lambda = 0.0001;
// A very simple fake dataset
arma::mat dataset = "2 0 0;"
"0 0 0;"
@@ -481,14 +459,46 @@ BOOST_AUTO_TEST_CASE(LinearSVMLGFGSSimpleTest)
// Corresponding labels
arma::Row<size_t> labels = "1 0 1";
// Create a linear svm object using a custom L-BFGS object.
LinearSVM<arma::mat> lsvm(dataset, labels, 2, 0.0001, ens::L_BFGS());
// Create a linear svm object using L-BFGS optimizer.
LinearSVM<arma::mat> lsvm(dataset, labels, numClasses, lambda,
ens::L_BFGS());
// Compare training accuracy to 100.
const double acc = lsvm.ComputeAccuracy(dataset, labels);
BOOST_REQUIRE_CLOSE(acc, 100.0, 0.5);
}
/**
* Test training of linear svm on a simple dataset using
* Gradient Descent optimizer
*/
BOOST_AUTO_TEST_CASE(LinearSVMGradientDescentSimpleTest)
{
const size_t numClasses = 2;
const size_t maxIterations = 10000;
const double stepSize = 0.01;
const double tolerance = 1e-5;
const double lambda = 0.0001;
// A very simple fake dataset
arma::mat dataset = "2 0 0;"
"0 0 0;"
"0 2 1;"
"1 0 2;"
"0 1 0";
// Corresponding labels
arma::Row<size_t> labels = "1 0 1";
// Create a linear svm object using custom gradient descent optimizer.
ens::GradientDescent optimizer(stepSize, maxIterations, tolerance);
LinearSVM<arma::mat> lsvm(dataset, labels, numClasses, lambda, optimizer);
// Compare training accuracy to 100.
const double acc = lsvm.ComputeAccuracy(dataset, labels);
BOOST_REQUIRE_CLOSE(acc, 100.0, 0.5);
}
/**
* Test training of linear svm for two classes on a complex gaussian dataset
* using L-BFGS optimizer.
@@ -518,8 +528,9 @@ BOOST_AUTO_TEST_CASE(LinearSVMLBFGSTwoClasses)
labels(i) = 1;
}
// Train linear svm object using L-BFGS optimizer.
LinearSVM<arma::mat> lsvm(data, labels, numClasses, lambda, ens::L_BFGS());
// Create a linear svm object using L-BFGS optimizer.
LinearSVM<arma::mat> lsvm(data, labels, numClasses, lambda,
ens::L_BFGS());
// Compare training accuracy to 100.
const double acc = lsvm.ComputeAccuracy(data, labels);
@@ -542,6 +553,108 @@ BOOST_AUTO_TEST_CASE(LinearSVMLBFGSTwoClasses)
BOOST_REQUIRE_CLOSE(testAcc, 100.0, 0.6);
}
/**
* The test is only compiled if the user has specified OpenMP to be
* used.
*/
#ifdef HAS_OPENMP
/**
* Test training of linear svm on a simple dataset using
* Parallel SGD optimizer.
*/
BOOST_AUTO_TEST_CASE(LinearSVMPSGDSimpleTest)
{
const size_t numClasses = 2;
const double lambda = 0.5;
const double alpha = 0.01;
// A very simple fake dataset
arma::mat dataset = "2 0 0;"
"0 0 0;"
"0 2 1;"
"1 0 2;"
"0 1 0";
// Corresponding labels
arma::Row<size_t> labels = "1 0 1";
ens::ConstantStep decayPolicy(alpha);
// Train linear svm object using Parallel SGD optimizer.
// The threadShareSize is chosen such that each function gets optimized.
ens::ParallelSGD<ens::ConstantStep> optimizer(0,
std::ceil((float) dataset.n_cols / omp_get_max_threads()),
1e-5, true, decayPolicy);
LinearSVM<arma::mat> lsvm(dataset, labels, numClasses, lambda, optimizer);
// Compare training accuracy to 100.
const double acc = lsvm.ComputeAccuracy(dataset, labels);
BOOST_REQUIRE_CLOSE(acc, 100.0, 0.5);
}
/**
* Test training of linear svm for two classes on a complex gaussian dataset
* using Parallel SGD optimizer.
*/
BOOST_AUTO_TEST_CASE(LinearSVMParallelSGDTwoClasses)
{
const size_t points = 500;
const size_t inputSize = 3;
const size_t numClasses = 2;
const double lambda = 0.5;
const double alpha = 0.01;
// Generate two-Gaussian dataset.
GaussianDistribution g1(arma::vec("1.0 9.0 1.0"), arma::eye<arma::mat>(3, 3));
GaussianDistribution g2(arma::vec("4.0 3.0 4.0"), arma::eye<arma::mat>(3, 3));
arma::mat data(inputSize, points);
arma::Row<size_t> labels(points);
for (size_t i = 0; i < points / 2; i++)
{
data.col(i) = g1.Random();
labels(i) = 0;
}
for (size_t i = points / 2; i < points; i++)
{
data.col(i) = g2.Random();
labels(i) = 1;
}
ens::ConstantStep decayPolicy(alpha);
// Train linear svm object using Parallel SGD optimizer.
// The threadShareSize is chosen such that each function gets optimized.
ens::ParallelSGD<ens::ConstantStep> optimizer(0,
std::ceil((float) data.n_cols / omp_get_max_threads()),
1e-5, true, decayPolicy);
LinearSVM<arma::mat> lsvm(data, labels, numClasses, lambda, optimizer);
// Compare training accuracy to 100.
const double acc = lsvm.ComputeAccuracy(data, labels);
BOOST_REQUIRE_CLOSE(acc, 100.0, 0.5);
// Create test dataset.
for (size_t i = 0; i < points / 2; i++)
{
data.col(i) = g1.Random();
labels(i) = 0;
}
for (size_t i = points / 2; i < points; i++)
{
data.col(i) = g2.Random();
labels(i) = 1;
}
// Compare test accuracy to 100.
const double testAcc = lsvm.ComputeAccuracy(data, labels);
BOOST_REQUIRE_CLOSE(testAcc, 100.0, 0.6);
}
#endif
/**
* Test sparse and dense linear svm and make sure they both work the
* same using the L-BFGS optimizer.
@@ -667,9 +780,9 @@ BOOST_AUTO_TEST_CASE(LinearSVMTrainTest)
LinearSVM<arma::mat> lsvm(dataset.n_rows, 2);
LinearSVM<arma::mat> lsvm2(dataset.n_rows, 2);
lsvm.Parameters() = lsvm2.Parameters();
ens::L_BFGS lbfgs;
lsvm.Train(dataset, labels, 2, std::move(lbfgs));
lsvm2.Train(dataset, labels, 2, std::move(lbfgs));
ens::L_BFGS optimizer;
lsvm.Train(dataset, labels, 2, std::move(optimizer));
lsvm2.Train(dataset, labels, 2, std::move(optimizer));
// Ensure that the parameters are the same.
BOOST_REQUIRE_EQUAL(lsvm.Parameters().n_rows, lsvm2.Parameters().n_rows);