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Merge pull request #15959 from mshabunin:refactor-ml-tests

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ml: refactored tests

* use parametrized tests where appropriate
* use stable theRNG in most tests
* use modern style with EXPECT_/ASSERT_ checks
This commit is contained in:
Maksim Shabunin
2019-11-25 20:03:16 +00:00
committed by Alexander Alekhin
parent 9e906d9e21
commit 5ff1fababc
16 changed files with 1418 additions and 2857 deletions
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modules/ml/test/test_svmtrainauto.cpp
+69 -120
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@@ -1,43 +1,6 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
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// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
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//
// Redistribution and use in source and binary forms, with or without modification,
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
@@ -46,21 +9,11 @@ namespace opencv_test { namespace {
using cv::ml::SVM;
using cv::ml::TrainData;
//--------------------------------------------------------------------------------------------
class CV_SVMTrainAutoTest : public cvtest::BaseTest {
public:
CV_SVMTrainAutoTest() {}
protected:
virtual void run( int start_from );
};
void CV_SVMTrainAutoTest::run( int /*start_from*/ )
static Ptr<TrainData> makeRandomData(int datasize)
{
int datasize = 100;
cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 );
cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S );
RNG rng(0);
RNG &rng = cv::theRNG();
for (int i = 0; i < datasize; ++i)
{
int response = rng.uniform(0, 2); // Random from {0, 1}.
@@ -68,36 +21,14 @@ void CV_SVMTrainAutoTest::run( int /*start_from*/ )
samples.at<float>( i, 1 ) = rng.uniform(0.f, 0.5f) + response * 0.5f;
responses.at<int>( i, 0 ) = response;
}
cv::Ptr<TrainData> data = TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
cv::Ptr<SVM> svm = SVM::create();
svm->trainAuto( data, 10 ); // 2-fold cross validation.
float test_data0[2] = {0.25f, 0.25f};
cv::Mat test_point0 = cv::Mat( 1, 2, CV_32FC1, test_data0 );
float result0 = svm->predict( test_point0 );
float test_data1[2] = {0.75f, 0.75f};
cv::Mat test_point1 = cv::Mat( 1, 2, CV_32FC1, test_data1 );
float result1 = svm->predict( test_point1 );
if ( fabs( result0 - 0 ) > 0.001 || fabs( result1 - 1 ) > 0.001 )
{
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
}
return TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
}
TEST(ML_SVM, trainauto) { CV_SVMTrainAutoTest test; test.safe_run(); }
TEST(ML_SVM, trainauto_sigmoid)
static Ptr<TrainData> makeCircleData(int datasize, float scale_factor, float radius)
{
const int datasize = 100;
// Populate samples with data that can be split into two concentric circles
cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 );
cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S );
const float scale_factor = 0.5;
const float radius = 2.0;
// Populate samples with data that can be split into two concentric circles
for (int i = 0; i < datasize; i+=2)
{
const float pi = 3.14159f;
@@ -115,32 +46,14 @@ TEST(ML_SVM, trainauto_sigmoid)
samples.at<float>( i + 1, 1 ) = y * scale_factor;
responses.at<int>( i + 1, 0 ) = 1;
}
cv::Ptr<TrainData> data = TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
cv::Ptr<SVM> svm = SVM::create();
svm->setKernel(SVM::SIGMOID);
svm->setGamma(10.0);
svm->setCoef0(-10.0);
svm->trainAuto( data, 10 ); // 2-fold cross validation.
float test_data0[2] = {radius, radius};
cv::Mat test_point0 = cv::Mat( 1, 2, CV_32FC1, test_data0 );
ASSERT_EQ(0, svm->predict( test_point0 ));
float test_data1[2] = {scale_factor * radius, scale_factor * radius};
cv::Mat test_point1 = cv::Mat( 1, 2, CV_32FC1, test_data1 );
ASSERT_EQ(1, svm->predict( test_point1 ));
return TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
}
TEST(ML_SVM, trainAuto_regression_5369)
static Ptr<TrainData> makeRandomData2(int datasize)
{
int datasize = 100;
cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 );
cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S );
RNG rng(0); // fixed!
RNG &rng = cv::theRNG();
for (int i = 0; i < datasize; ++i)
{
int response = rng.uniform(0, 2); // Random from {0, 1}.
@@ -148,8 +61,59 @@ TEST(ML_SVM, trainAuto_regression_5369)
samples.at<float>( i, 1 ) = (0.5f - response) * rng.uniform(0.f, 1.2f) + response;
responses.at<int>( i, 0 ) = response;
}
return TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
}
cv::Ptr<TrainData> data = TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
//==================================================================================================
TEST(ML_SVM, trainauto)
{
const int datasize = 100;
cv::Ptr<TrainData> data = makeRandomData(datasize);
ASSERT_TRUE(data);
cv::Ptr<SVM> svm = SVM::create();
ASSERT_TRUE(svm);
svm->trainAuto( data, 10 ); // 2-fold cross validation.
float test_data0[2] = {0.25f, 0.25f};
cv::Mat test_point0 = cv::Mat( 1, 2, CV_32FC1, test_data0 );
float result0 = svm->predict( test_point0 );
float test_data1[2] = {0.75f, 0.75f};
cv::Mat test_point1 = cv::Mat( 1, 2, CV_32FC1, test_data1 );
float result1 = svm->predict( test_point1 );
EXPECT_NEAR(result0, 0, 0.001);
EXPECT_NEAR(result1, 1, 0.001);
}
TEST(ML_SVM, trainauto_sigmoid)
{
const int datasize = 100;
const float scale_factor = 0.5;
const float radius = 2.0;
cv::Ptr<TrainData> data = makeCircleData(datasize, scale_factor, radius);
ASSERT_TRUE(data);
cv::Ptr<SVM> svm = SVM::create();
ASSERT_TRUE(svm);
svm->setKernel(SVM::SIGMOID);
svm->setGamma(10.0);
svm->setCoef0(-10.0);
svm->trainAuto( data, 10 ); // 2-fold cross validation.
float test_data0[2] = {radius, radius};
cv::Mat test_point0 = cv::Mat( 1, 2, CV_32FC1, test_data0 );
EXPECT_FLOAT_EQ(svm->predict( test_point0 ), 0);
float test_data1[2] = {scale_factor * radius, scale_factor * radius};
cv::Mat test_point1 = cv::Mat( 1, 2, CV_32FC1, test_data1 );
EXPECT_FLOAT_EQ(svm->predict( test_point1 ), 1);
}
TEST(ML_SVM, trainAuto_regression_5369)
{
const int datasize = 100;
Ptr<TrainData> data = makeRandomData2(datasize);
cv::Ptr<SVM> svm = SVM::create();
svm->trainAuto( data, 10 ); // 2-fold cross validation.
@@ -164,16 +128,8 @@ TEST(ML_SVM, trainAuto_regression_5369)
EXPECT_EQ(1., result1);
}
class CV_SVMGetSupportVectorsTest : public cvtest::BaseTest {
public:
CV_SVMGetSupportVectorsTest() {}
protected:
virtual void run( int startFrom );
};
void CV_SVMGetSupportVectorsTest::run(int /*startFrom*/ )
TEST(ML_SVM, getSupportVectors)
{
int code = cvtest::TS::OK;
// Set up training data
int labels[4] = {1, -1, -1, -1};
float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };
@@ -181,19 +137,18 @@ void CV_SVMGetSupportVectorsTest::run(int /*startFrom*/ )
Mat labelsMat(4, 1, CV_32SC1, labels);
Ptr<SVM> svm = SVM::create();
ASSERT_TRUE(svm);
svm->setType(SVM::C_SVC);
svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));
// Test retrieval of SVs and compressed SVs on linear SVM
svm->setKernel(SVM::LINEAR);
svm->train(trainingDataMat, cv::ml::ROW_SAMPLE, labelsMat);
Mat sv = svm->getSupportVectors();
CV_Assert(sv.rows == 1); // by default compressed SV returned
EXPECT_EQ(1, sv.rows); // by default compressed SV returned
sv = svm->getUncompressedSupportVectors();
CV_Assert(sv.rows == 3);
EXPECT_EQ(3, sv.rows);
// Test retrieval of SVs and compressed SVs on non-linear SVM
svm->setKernel(SVM::POLY);
@@ -201,15 +156,9 @@ void CV_SVMGetSupportVectorsTest::run(int /*startFrom*/ )
svm->train(trainingDataMat, cv::ml::ROW_SAMPLE, labelsMat);
sv = svm->getSupportVectors();
CV_Assert(sv.rows == 3);
EXPECT_EQ(3, sv.rows);
sv = svm->getUncompressedSupportVectors();
CV_Assert(sv.rows == 0); // inapplicable for non-linear SVMs
ts->set_failed_test_info(code);
EXPECT_EQ(0, sv.rows); // inapplicable for non-linear SVMs
}
TEST(ML_SVM, getSupportVectors) { CV_SVMGetSupportVectorsTest test; test.safe_run(); }
}} // namespace