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Merge pull request #1220 from nikhilgoel1997/AdaBoostBindingTest

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AdaBoost binding tests
This commit is contained in:
Ryan Curtin
2018-02-07 07:40:53 -08:00
committed by GitHub
parent 4f664d208d ad8e39143e
commit 8403b92d17
4 changed files with 418 additions and 0 deletions
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COPYRIGHT.txt
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@@ -87,6 +87,7 @@ Copyright:
Copyright 2017, Manish Kumar <manish887kr@gmail.com>
Copyright 2017, Haritha Sreedharan Nair <haritha1313@gmail.com>
Copyright 2017&2018, Sourabh Varshney <sourabhvarshney111@gmail.com>
Copyright 2018, Nikhil Goel <nikhilgoel199797@gmail.com>
License: BSD-3-clause
All rights reserved.
src/mlpack/core.hpp
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@@ -230,6 +230,7 @@
* - Manish Kumar <manish887kr@gmail.com>
* - Haritha Sreedharan Nair <haritha1313@gmail.com>
* - Sourabh Varshney <sourabhvarshney111@gmail.com>
* - Nikhil Goel <nikhilgoel199797@gmail.com>
*/
// First, include all of the prerequisites.
src/mlpack/tests/CMakeLists.txt
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@@ -125,6 +125,7 @@ add_executable(mlpack_test
vantage_point_tree_test.cpp
main_tests/test_helper.hpp
main_tests/emst_test.cpp
main_tests/adaboost_test.cpp
main_tests/decision_tree_test.cpp
main_tests/decision_stump_test.cpp
main_tests/linear_regression_test.cpp
src/mlpack/tests/main_tests/adaboost_test.cpp
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@@ -0,0 +1,415 @@
/**
* @file adaboost_test.cpp
* @author Nikhil Goel
*
* Test mlpackMain() of adaboost_main.cpp.
*/
#include <string>
#define BINDING_TYPE BINDING_TYPE_TEST
static const std::string testName = "AdaBoost";
#include <mlpack/core.hpp>
#include <mlpack/core/util/mlpack_main.hpp>
#include "test_helper.hpp"
#include <mlpack/methods/adaboost/adaboost_main.cpp>
#include <boost/test/unit_test.hpp>
#include "../test_tools.hpp"
using namespace mlpack;
struct AdaBoostTestFixture
{
public:
AdaBoostTestFixture()
{
// Cache in the options for this program.
CLI::RestoreSettings(testName);
}
~AdaBoostTestFixture()
{
// Clear the settings.
bindings::tests::CleanMemory();
CLI::ClearSettings();
}
};
BOOST_FIXTURE_TEST_SUITE(AdaBoostMainTest, AdaBoostTestFixture);
/**
* Check that number of output labels and number of input
* points are equal.
*/
BOOST_AUTO_TEST_CASE(AdaBoostOutputDimensionTest)
{
arma::mat trainData;
if (!data::Load("vc2.csv", trainData))
BOOST_FAIL("Unable to load train dataset vc2.csv!");
arma::Row<size_t> labels;
if (!data::Load("vc2_labels.txt", labels))
BOOST_FAIL("Unable to load label dataset vc2_labels.txt!");
arma::mat testData;
if (!data::Load("vc2_test.csv", testData))
BOOST_FAIL("Unable to load test dataset vc2.csv!");
size_t testSize = testData.n_cols;
SetInputParam("training", std::move(trainData));
SetInputParam("labels", std::move(labels));
SetInputParam("test", std::move(testData));
mlpackMain();
// Check that number of predicted labels is equal to the input test points.
BOOST_REQUIRE_EQUAL(CLI::GetParam<arma::Row<size_t>>("output").n_cols,
testSize);
BOOST_REQUIRE_EQUAL(CLI::GetParam<arma::Row<size_t>>("output").n_rows, 1);
}
/**
* Ensure that saved model can be used again.
*/
BOOST_AUTO_TEST_CASE(AdaBoostModelReuseTest)
{
arma::mat trainData;
if (!data::Load("vc2.csv", trainData))
BOOST_FAIL("Unable to load train dataset vc2.csv!");
arma::Row<size_t> labels;
if (!data::Load("vc2_labels.txt", labels))
BOOST_FAIL("Unable to load label dataset vc2_labels.txt!");
arma::mat testData;
if (!data::Load("vc2_test.csv", testData))
BOOST_FAIL("Unable to load test dataset vc2.csv!");
SetInputParam("training", std::move(trainData));
SetInputParam("labels", std::move(labels));
SetInputParam("test", testData);
mlpackMain();
arma::Row<size_t> output;
output = std::move(CLI::GetParam<arma::Row<size_t>>("output"));
CLI::GetSingleton().Parameters()["training"].wasPassed = false;
CLI::GetSingleton().Parameters()["labels"].wasPassed = false;
CLI::GetSingleton().Parameters()["test"].wasPassed = false;
SetInputParam("test", std::move(testData));
SetInputParam("input_model",
CLI::GetParam<AdaBoostModel*>("output_model"));
mlpackMain();
// Check that initial output and output using saved model are same.
CheckMatrices(output, CLI::GetParam<arma::Row<size_t>>("output"));
}
/**
* Test that iterations in adaboost is always non-negative.
*/
BOOST_AUTO_TEST_CASE(AdaBoostItrTest)
{
arma::mat trainData;
if (!data::Load("trainSet.csv", trainData))
BOOST_FAIL("Unable load train dataset trainSet.csv!");
SetInputParam("training", std::move(trainData));
SetInputParam("iterations", (int) -1);
Log::Fatal.ignoreInput = true;
BOOST_REQUIRE_THROW(mlpackMain(), std::runtime_error);
Log::Fatal.ignoreInput = false;
}
/**
* Check that the last dimension of the training set is
* used as labels when labels are not passed specifically
* and results are same from both label and without label models.
*/
BOOST_AUTO_TEST_CASE(AdaBoostWithoutLabelTest)
{
// Train adaboost without providing labels.
arma::mat trainData;
if (!data::Load("trainSet.csv", trainData))
BOOST_FAIL("Unable to load train dataset trainSet.csv!");
// Give labels.
arma::Row<size_t> labels(trainData.n_cols);
for (size_t i = 0; i < trainData.n_cols; ++i)
labels[i] = trainData(trainData.n_rows - 1, i);
arma::mat testData;
if (!data::Load("testSet.csv", testData))
BOOST_FAIL("Unable to load test dataset testSet.csv!");
// Delete the last row containing labels from test dataset.
testData.shed_row(testData.n_rows - 1);
SetInputParam("training", trainData);
SetInputParam("test", testData);
mlpackMain();
CLI::GetSingleton().Parameters()["training"].wasPassed = false;
CLI::GetSingleton().Parameters()["test"].wasPassed = false;
arma::Row<size_t> output;
output = std::move(CLI::GetParam<arma::Row<size_t>>("output"));
bindings::tests::CleanMemory();
trainData.shed_row(trainData.n_rows - 1);
// Now train Adaboost with labels provided.
SetInputParam("training", std::move(trainData));
SetInputParam("test", std::move(testData));
SetInputParam("labels", std::move(labels));
mlpackMain();
// Check that initial output and final output matrix are same.
CheckMatrices(output, CLI::GetParam<arma::Row<size_t>>("output"));
}
/**
* Testing that only one of training data or pre-trained model is passed.
*/
BOOST_AUTO_TEST_CASE(AdaBoostTrainingDataOrModelTest)
{
arma::mat trainData;
if (!data::Load("trainSet.csv", trainData))
BOOST_FAIL("Unable to load train dataset trainSet.csv!");
SetInputParam("training", std::move(trainData));
mlpackMain();
SetInputParam("input_model",
CLI::GetParam<AdaBoostModel*>("output_model"));
Log::Fatal.ignoreInput = true;
BOOST_REQUIRE_THROW(mlpackMain(), std::runtime_error);
Log::Fatal.ignoreInput = false;
}
/**
* Weak learner should be either Decision Stump or Perceptron.
*/
BOOST_AUTO_TEST_CASE(AdaBoostWeakLearnerTest)
{
arma::mat trainData;
if (!data::Load("trainSet.csv", trainData))
BOOST_FAIL("Unable to load train dataset trainSet.csv!");
SetInputParam("training", std::move(trainData));
SetInputParam("weak_learner", std::string("decision tree"));
Log::Fatal.ignoreInput = true;
BOOST_REQUIRE_THROW(mlpackMain(), std::runtime_error);
Log::Fatal.ignoreInput = false;
}
/**
* Different Weak learner should give different outputs.
*/
BOOST_AUTO_TEST_CASE(AdaBoostDiffWeakLearnerOutputTest)
{
arma::mat trainData;
if (!data::Load("vc2.csv", trainData))
BOOST_FAIL("Unable to load train dataset vc2.csv!");
arma::Row<size_t> labels;
if (!data::Load("vc2_labels.txt", labels))
BOOST_FAIL("Unable to load label dataset vc2_labels.txt!");
arma::mat testData;
if (!data::Load("vc2_test.csv", testData))
BOOST_FAIL("Unable to load test dataset vc2.csv!");
SetInputParam("training", trainData);
SetInputParam("labels", labels);
SetInputParam("test", testData);
mlpackMain();
arma::Row<size_t> output;
output = std::move(CLI::GetParam<arma::Row<size_t>>("output"));
bindings::tests::CleanMemory();
CLI::GetSingleton().Parameters()["training"].wasPassed = false;
CLI::GetSingleton().Parameters()["labels"].wasPassed = false;
CLI::GetSingleton().Parameters()["test"].wasPassed = false;
SetInputParam("training", trainData);
SetInputParam("labels", labels);
SetInputParam("test", testData);
SetInputParam("weak_learner", std::string("perceptron"));
mlpackMain();
arma::Row<size_t> outputPerceptron;
outputPerceptron = std::move(CLI::GetParam<arma::Row<size_t>>("output"));
BOOST_REQUIRE_GT(arma::accu(output != outputPerceptron), 1);
}
/**
* Accuracy increases as Number of Iterations increases.
* (Or converges and remains same)
*/
BOOST_AUTO_TEST_CASE(AdaBoostDiffItrTest)
{
arma::mat trainData;
if (!data::Load("vc2.csv", trainData))
BOOST_FAIL("Unable to load train dataset vc2.csv!");
arma::Row<size_t> labels;
if (!data::Load("vc2_labels.txt", labels))
BOOST_FAIL("Unable to load label dataset vc2_labels.txt!");
arma::mat testData;
if (!data::Load("vc2_test.csv", testData))
BOOST_FAIL("Unable to load test dataset vc2.csv!");
arma::Row<size_t> testLabels;
if (!data::Load("vc2_test_labels.txt", testLabels))
BOOST_FAIL("Unable to load labels for vc2__test_labels.txt");
// Iterations = 1
SetInputParam("training", trainData);
SetInputParam("labels", labels);
SetInputParam("weak_learner", std::string("perceptron"));
SetInputParam("iterations", (int) 1);
mlpackMain();
// Calculate accuracy.
arma::Row<size_t> output;
CLI::GetParam<AdaBoostModel*>("output_model")->Classify(testData,
output);
size_t correct = arma::accu(output == testLabels);
double accuracy1 = (double(correct) / double(testLabels.n_elem) * 100);
bindings::tests::CleanMemory();
// Iterations = 10
SetInputParam("training", trainData);
SetInputParam("labels", labels);
SetInputParam("weak_learner", std::string("perceptron"));
SetInputParam("iterations", (int) 10);
mlpackMain();
// Calculate accuracy.
CLI::GetParam<AdaBoostModel*>("output_model")->Classify(testData,
output);
correct = arma::accu(output == testLabels);
double accuracy10 = (double(correct) / double(testLabels.n_elem) * 100);
bindings::tests::CleanMemory();
// Iterations = 100
SetInputParam("training", trainData);
SetInputParam("labels", labels);
SetInputParam("weak_learner", std::string("perceptron"));
SetInputParam("iterations", (int) 100);
mlpackMain();
// Calculate accuracy.
CLI::GetParam<AdaBoostModel*>("output_model")->Classify(testData,
output);
correct = arma::accu(output == testLabels);
double accuracy100 = (double(correct) / double(testLabels.n_elem) * 100);
BOOST_REQUIRE_LE(accuracy1, accuracy10);
BOOST_REQUIRE_LE(accuracy10, accuracy100);
}
/**
* Accuracy increases as tolerance decreases.
* (Execution Time also increases)
*/
BOOST_AUTO_TEST_CASE(AdaBoostDiffTolTest)
{
arma::mat trainData;
if (!data::Load("vc2.csv", trainData))
BOOST_FAIL("Unable to load train dataset vc2.csv!");
arma::Row<size_t> labels;
if (!data::Load("vc2_labels.txt", labels))
BOOST_FAIL("Unable to load label dataset vc2_labels.txt!");
arma::mat testData;
if (!data::Load("vc2_test.csv", testData))
BOOST_FAIL("Unable to load test dataset vc2.csv!");
arma::Row<size_t> testLabels;
if (!data::Load("vc2_test_labels.txt", testLabels))
BOOST_FAIL("Unable to load labels for vc2__test_labels.txt");
// tolerance = 0.001
SetInputParam("training", trainData);
SetInputParam("labels", labels);
SetInputParam("tolerance", (double) 0.001);
mlpackMain();
// Calculate accuracy.
arma::Row<size_t> output;
CLI::GetParam<AdaBoostModel*>("output_model")->Classify(testData,
output);
size_t correct = arma::accu(output == testLabels);
double accuracy1 = (double(correct) / double(testLabels.n_elem) * 100);
bindings::tests::CleanMemory();
// tolerance = 0.01
SetInputParam("training", trainData);
SetInputParam("labels", labels);
SetInputParam("tolerance", (double) 0.01);
mlpackMain();
// Calculate accuracy.
CLI::GetParam<AdaBoostModel*>("output_model")->Classify(testData,
output);
correct = arma::accu(output == testLabels);
double accuracy2 = (double(correct) / double(testLabels.n_elem) * 100);
bindings::tests::CleanMemory();
// tolerance = 0.1
SetInputParam("training", trainData);
SetInputParam("labels", labels);
SetInputParam("tolerance", (double) 0.1);
mlpackMain();
// Calculate accuracy.
CLI::GetParam<AdaBoostModel*>("output_model")->Classify(testData,
output);
correct = arma::accu(output == testLabels);
double accuracy3 = (double(correct) / double(testLabels.n_elem) * 100);
BOOST_REQUIRE_LE(accuracy1, accuracy2);
BOOST_REQUIRE_LE(accuracy2, accuracy3);
}
BOOST_AUTO_TEST_SUITE_END();