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Merge pull request #15959 from mshabunin:refactor-ml-tests
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
committed by
Alexander Alekhin
Alexander Alekhin
parent
9e906d9e21
commit
5ff1fababc
+67
-229
@@ -1,281 +1,119 @@
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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// Redistribution and use in source and binary forms, with or without modification,
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// * Redistribution's of source code must retain the above copyright notice,
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// * The name of Intel Corporation may not be used to endorse or promote products
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// This software is provided by the copyright holders and contributors "as is" and
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//M*/
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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using cv::ml::SVMSGD;
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using cv::ml::TrainData;
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class CV_SVMSGDTrainTest : public cvtest::BaseTest
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static const int TEST_VALUE_LIMIT = 500;
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enum
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{
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public:
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enum TrainDataType
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{
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UNIFORM_SAME_SCALE,
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UNIFORM_DIFFERENT_SCALES
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};
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CV_SVMSGDTrainTest(const Mat &_weights, float shift, TrainDataType type, double precision = 0.01);
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private:
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virtual void run( int start_from );
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static float decisionFunction(const Mat &sample, const Mat &weights, float shift);
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void makeData(int samplesCount, const Mat &weights, float shift, RNG &rng, Mat &samples, Mat & responses);
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void generateSameBorders(int featureCount);
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void generateDifferentBorders(int featureCount);
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TrainDataType type;
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double precision;
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std::vector<std::pair<float,float> > borders;
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cv::Ptr<TrainData> data;
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cv::Mat testSamples;
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cv::Mat testResponses;
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static const int TEST_VALUE_LIMIT = 500;
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UNIFORM_SAME_SCALE,
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UNIFORM_DIFFERENT_SCALES
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};
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void CV_SVMSGDTrainTest::generateSameBorders(int featureCount)
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{
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float lowerLimit = -TEST_VALUE_LIMIT;
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float upperLimit = TEST_VALUE_LIMIT;
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CV_ENUM(SVMSGD_TYPE, UNIFORM_SAME_SCALE, UNIFORM_DIFFERENT_SCALES)
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for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
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{
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borders.push_back(std::pair<float,float>(lowerLimit, upperLimit));
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}
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}
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typedef std::vector< std::pair<float,float> > BorderList;
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void CV_SVMSGDTrainTest::generateDifferentBorders(int featureCount)
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{
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float lowerLimit = -TEST_VALUE_LIMIT;
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float upperLimit = TEST_VALUE_LIMIT;
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cv::RNG rng(0);
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for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
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{
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int crit = rng.uniform(0, 2);
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if (crit > 0)
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{
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borders.push_back(std::pair<float,float>(lowerLimit, upperLimit));
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}
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else
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{
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borders.push_back(std::pair<float,float>(lowerLimit/1000, upperLimit/1000));
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}
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}
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}
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float CV_SVMSGDTrainTest::decisionFunction(const Mat &sample, const Mat &weights, float shift)
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{
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return static_cast<float>(sample.dot(weights)) + shift;
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}
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void CV_SVMSGDTrainTest::makeData(int samplesCount, const Mat &weights, float shift, RNG &rng, Mat &samples, Mat & responses)
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static void makeData(RNG &rng, int samplesCount, const Mat &weights, float shift, const BorderList & borders, Mat &samples, Mat & responses)
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{
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int featureCount = weights.cols;
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samples.create(samplesCount, featureCount, CV_32FC1);
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for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
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{
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rng.fill(samples.col(featureIndex), RNG::UNIFORM, borders[featureIndex].first, borders[featureIndex].second);
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}
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responses.create(samplesCount, 1, CV_32FC1);
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for (int i = 0 ; i < samplesCount; i++)
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{
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responses.at<float>(i) = decisionFunction(samples.row(i), weights, shift) > 0 ? 1.f : -1.f;
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double res = samples.row(i).dot(weights) + shift;
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responses.at<float>(i) = res > 0 ? 1.f : -1.f;
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}
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}
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CV_SVMSGDTrainTest::CV_SVMSGDTrainTest(const Mat &weights, float shift, TrainDataType _type, double _precision)
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//==================================================================================================
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typedef tuple<SVMSGD_TYPE, int, double> ML_SVMSGD_Param;
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typedef testing::TestWithParam<ML_SVMSGD_Param> ML_SVMSGD_Params;
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TEST_P(ML_SVMSGD_Params, scale_and_features)
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{
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type = _type;
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precision = _precision;
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const int type = get<0>(GetParam());
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const int featureCount = get<1>(GetParam());
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const double precision = get<2>(GetParam());
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int featureCount = weights.cols;
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RNG &rng = cv::theRNG();
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switch(type)
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Mat_<float> weights(1, featureCount);
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rng.fill(weights, RNG::UNIFORM, -1, 1);
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const float shift = static_cast<float>(rng.uniform(-featureCount, featureCount));
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BorderList borders;
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float lowerLimit = -TEST_VALUE_LIMIT;
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float upperLimit = TEST_VALUE_LIMIT;
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if (type == UNIFORM_SAME_SCALE)
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{
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case UNIFORM_SAME_SCALE:
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generateSameBorders(featureCount);
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break;
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case UNIFORM_DIFFERENT_SCALES:
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generateDifferentBorders(featureCount);
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break;
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default:
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CV_Error(CV_StsBadArg, "Unknown train data type");
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for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
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borders.push_back(std::pair<float,float>(lowerLimit, upperLimit));
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}
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RNG rng(0);
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else if (type == UNIFORM_DIFFERENT_SCALES)
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{
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for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
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{
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int crit = rng.uniform(0, 2);
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if (crit > 0)
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borders.push_back(std::pair<float,float>(lowerLimit, upperLimit));
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else
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borders.push_back(std::pair<float,float>(lowerLimit/1000, upperLimit/1000));
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}
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}
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ASSERT_FALSE(borders.empty());
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Mat trainSamples;
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Mat trainResponses;
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int trainSamplesCount = 10000;
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makeData(trainSamplesCount, weights, shift, rng, trainSamples, trainResponses);
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data = TrainData::create(trainSamples, cv::ml::ROW_SAMPLE, trainResponses);
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makeData(rng, trainSamplesCount, weights, shift, borders, trainSamples, trainResponses);
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ASSERT_EQ(trainResponses.type(), CV_32FC1);
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Mat testSamples;
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Mat testResponses;
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int testSamplesCount = 100000;
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makeData(testSamplesCount, weights, shift, rng, testSamples, testResponses);
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}
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makeData(rng, testSamplesCount, weights, shift, borders, testSamples, testResponses);
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ASSERT_EQ(testResponses.type(), CV_32FC1);
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Ptr<TrainData> data = TrainData::create(trainSamples, cv::ml::ROW_SAMPLE, trainResponses);
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ASSERT_TRUE(data);
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void CV_SVMSGDTrainTest::run( int /*start_from*/ )
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{
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cv::Ptr<SVMSGD> svmsgd = SVMSGD::create();
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ASSERT_TRUE(svmsgd);
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svmsgd->train(data);
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Mat responses;
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svmsgd->predict(testSamples, responses);
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ASSERT_EQ(responses.type(), CV_32FC1);
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ASSERT_EQ(responses.rows, testSamplesCount);
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int errCount = 0;
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int testSamplesCount = testSamples.rows;
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CV_Assert((responses.type() == CV_32FC1) && (testResponses.type() == CV_32FC1));
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for (int i = 0; i < testSamplesCount; i++)
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{
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if (responses.at<float>(i) * testResponses.at<float>(i) < 0)
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errCount++;
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}
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float err = (float)errCount / testSamplesCount;
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if ( err > precision )
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{
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
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}
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EXPECT_LE(err, precision);
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}
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void makeWeightsAndShift(int featureCount, Mat &weights, float &shift)
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{
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weights.create(1, featureCount, CV_32FC1);
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cv::RNG rng(0);
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double lowerLimit = -1;
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double upperLimit = 1;
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ML_SVMSGD_Param params_list[] = {
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ML_SVMSGD_Param(UNIFORM_SAME_SCALE, 2, 0.01),
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ML_SVMSGD_Param(UNIFORM_SAME_SCALE, 5, 0.01),
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ML_SVMSGD_Param(UNIFORM_SAME_SCALE, 100, 0.02),
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ML_SVMSGD_Param(UNIFORM_DIFFERENT_SCALES, 2, 0.01),
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ML_SVMSGD_Param(UNIFORM_DIFFERENT_SCALES, 5, 0.01),
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ML_SVMSGD_Param(UNIFORM_DIFFERENT_SCALES, 100, 0.01),
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};
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rng.fill(weights, RNG::UNIFORM, lowerLimit, upperLimit);
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shift = static_cast<float>(rng.uniform(-featureCount, featureCount));
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}
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INSTANTIATE_TEST_CASE_P(/**/, ML_SVMSGD_Params, testing::ValuesIn(params_list));
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TEST(ML_SVMSGD, trainSameScale2)
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{
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int featureCount = 2;
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Mat weights;
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float shift = 0;
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makeWeightsAndShift(featureCount, weights, shift);
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CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_SAME_SCALE);
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test.safe_run();
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}
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TEST(ML_SVMSGD, trainSameScale5)
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{
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int featureCount = 5;
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Mat weights;
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float shift = 0;
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makeWeightsAndShift(featureCount, weights, shift);
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CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_SAME_SCALE);
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test.safe_run();
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}
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TEST(ML_SVMSGD, trainSameScale100)
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{
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int featureCount = 100;
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Mat weights;
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float shift = 0;
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makeWeightsAndShift(featureCount, weights, shift);
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CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_SAME_SCALE, 0.02);
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test.safe_run();
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}
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TEST(ML_SVMSGD, trainDifferentScales2)
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{
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int featureCount = 2;
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Mat weights;
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float shift = 0;
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makeWeightsAndShift(featureCount, weights, shift);
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CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_DIFFERENT_SCALES, 0.01);
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test.safe_run();
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}
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TEST(ML_SVMSGD, trainDifferentScales5)
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{
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int featureCount = 5;
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Mat weights;
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float shift = 0;
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makeWeightsAndShift(featureCount, weights, shift);
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CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_DIFFERENT_SCALES, 0.01);
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test.safe_run();
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}
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TEST(ML_SVMSGD, trainDifferentScales100)
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{
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int featureCount = 100;
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Mat weights;
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float shift = 0;
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makeWeightsAndShift(featureCount, weights, shift);
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CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_DIFFERENT_SCALES, 0.01);
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test.safe_run();
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}
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//==================================================================================================
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TEST(ML_SVMSGD, twoPoints)
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{
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