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mdlp/tests/BinDisc_unittest.cpp

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#include <fstream>
#include <string>
#include <iostream>
#include "gtest/gtest.h"
#include "ArffFiles.h"
#include "../BinDisc.h"
#include "Experiments.hpp"
namespace mdlp {
const float margin = 1e-4;
static std::string set_data_path()
{
std::string path = "../datasets/";
std::ifstream file(path + "iris.arff");
if (file.is_open()) {
file.close();
return path;
}
return "../../tests/datasets/";
}
const std::string data_path = set_data_path();
class TestBinDisc3U : public BinDisc, public testing::Test {
public:
TestBinDisc3U(int n_bins = 3) : BinDisc(n_bins, strategy_t::UNIFORM) {};
};
class TestBinDisc3Q : public BinDisc, public testing::Test {
public:
TestBinDisc3Q(int n_bins = 3) : BinDisc(n_bins, strategy_t::QUANTILE) {};
};
class TestBinDisc4U : public BinDisc, public testing::Test {
public:
TestBinDisc4U(int n_bins = 4) : BinDisc(n_bins, strategy_t::UNIFORM) {};
};
class TestBinDisc4Q : public BinDisc, public testing::Test {
public:
TestBinDisc4Q(int n_bins = 4) : BinDisc(n_bins, strategy_t::QUANTILE) {};
};
TEST_F(TestBinDisc3U, Easy3BinsUniform)
{
samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 };
auto y = labels_t();
fit(X, y);
auto cuts = getCutPoints();
ASSERT_EQ(4, cuts.size());
EXPECT_NEAR(1, cuts.at(0), margin);
EXPECT_NEAR(3.66667, cuts.at(1), margin);
EXPECT_NEAR(6.33333, cuts.at(2), margin);
EXPECT_NEAR(9.0, cuts.at(3), margin);
auto labels = transform(X);
labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc3Q, Easy3BinsQuantile)
{
samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 };
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(4, cuts.size());
EXPECT_NEAR(1, cuts[0], margin);
EXPECT_NEAR(3.666667, cuts[1], margin);
EXPECT_NEAR(6.333333, cuts[2], margin);
EXPECT_NEAR(9, cuts[3], margin);
auto labels = transform(X);
labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc3U, X10BinsUniform)
{
samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0 };
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(4, cuts.size());
EXPECT_NEAR(1, cuts.at(0), margin);
EXPECT_NEAR(4.0, cuts.at(1), margin);
EXPECT_NEAR(7.0, cuts.at(2), margin);
EXPECT_NEAR(10.0, cuts.at(3), margin);
auto labels = transform(X);
labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc3Q, X10BinsQuantile)
{
samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0 };
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(4, cuts.size());
EXPECT_NEAR(1, cuts.at(0), margin);
EXPECT_NEAR(4.0, cuts.at(1), margin);
EXPECT_NEAR(7.0, cuts.at(2), margin);
EXPECT_NEAR(10.0, cuts.at(3), margin);
auto labels = transform(X);
labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc3U, X11BinsUniform)
{
samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0 };
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(4, cuts.size());
EXPECT_NEAR(1, cuts.at(0), margin);
EXPECT_NEAR(4.33333, cuts.at(1), margin);
EXPECT_NEAR(7.66667, cuts.at(2), margin);
EXPECT_NEAR(11.0, cuts.at(3), margin);
auto labels = transform(X);
labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc3U, X11BinsQuantile)
{
samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0 };
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(4, cuts.size());
EXPECT_NEAR(1, cuts.at(0), margin);
EXPECT_NEAR(4.33333, cuts.at(1), margin);
EXPECT_NEAR(7.66667, cuts.at(2), margin);
EXPECT_NEAR(11.0, cuts.at(3), margin);
auto labels = transform(X);
labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc3U, ConstantUniform)
{
samples_t X = { 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(2, cuts.size());
EXPECT_NEAR(1, cuts.at(0), margin);
EXPECT_NEAR(1, cuts.at(1), margin);
auto labels = transform(X);
labels_t expected = { 0, 0, 0, 0, 0, 0 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc3Q, ConstantQuantile)
{
samples_t X = { 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(2, cuts.size());
EXPECT_NEAR(1, cuts.at(0), margin);
EXPECT_NEAR(1, cuts.at(1), margin);
auto labels = transform(X);
labels_t expected = { 0, 0, 0, 0, 0, 0 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc3U, EmptyUniform)
{
samples_t X = {};
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(2, cuts.size());
EXPECT_NEAR(0, cuts.at(0), margin);
EXPECT_NEAR(0, cuts.at(1), margin);
}
TEST_F(TestBinDisc3Q, EmptyQuantile)
{
samples_t X = {};
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(2, cuts.size());
EXPECT_NEAR(0, cuts.at(0), margin);
EXPECT_NEAR(0, cuts.at(1), margin);
}
TEST(TestBinDisc3, ExceptionNumberBins)
{
EXPECT_THROW(BinDisc(2), std::invalid_argument);
}
TEST_F(TestBinDisc3U, EasyRepeated)
{
samples_t X = { 3.0, 1.0, 1.0, 3.0, 1.0, 1.0, 3.0, 1.0, 1.0 };
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(4, cuts.size());
EXPECT_NEAR(1, cuts.at(0), margin);
EXPECT_NEAR(1.66667, cuts.at(1), margin);
EXPECT_NEAR(2.33333, cuts.at(2), margin);
EXPECT_NEAR(3.0, cuts.at(3), margin);
auto labels = transform(X);
labels_t expected = { 2, 0, 0, 2, 0, 0, 2, 0, 0 };
EXPECT_EQ(expected, labels);
ASSERT_EQ(3.0, X[0]); // X is not modified
}
TEST_F(TestBinDisc3Q, EasyRepeated)
{
samples_t X = { 3.0, 1.0, 1.0, 3.0, 1.0, 1.0, 3.0, 1.0, 1.0 };
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(3, cuts.size());
EXPECT_NEAR(1, cuts.at(0), margin);
EXPECT_NEAR(1.66667, cuts.at(1), margin);
EXPECT_NEAR(3.0, cuts.at(2), margin);
auto labels = transform(X);
labels_t expected = { 1, 0, 0, 1, 0, 0, 1, 0, 0 };
EXPECT_EQ(expected, labels);
ASSERT_EQ(3.0, X[0]); // X is not modified
}
TEST_F(TestBinDisc4U, Easy4BinsUniform)
{
samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 };
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(5, cuts.size());
EXPECT_NEAR(1.0, cuts.at(0), margin);
EXPECT_NEAR(3.75, cuts.at(1), margin);
EXPECT_NEAR(6.5, cuts.at(2), margin);
EXPECT_NEAR(9.25, cuts.at(3), margin);
EXPECT_NEAR(12.0, cuts.at(4), margin);
auto labels = transform(X);
labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc4Q, Easy4BinsQuantile)
{
samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 };
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(5, cuts.size());
EXPECT_NEAR(1.0, cuts.at(0), margin);
EXPECT_NEAR(3.75, cuts.at(1), margin);
EXPECT_NEAR(6.5, cuts.at(2), margin);
EXPECT_NEAR(9.25, cuts.at(3), margin);
EXPECT_NEAR(12.0, cuts.at(4), margin);
auto labels = transform(X);
labels_t expected = { 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc4U, X13BinsUniform)
{
samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0 };
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(5, cuts.size());
EXPECT_NEAR(1.0, cuts.at(0), margin);
EXPECT_NEAR(4.0, cuts.at(1), margin);
EXPECT_NEAR(7.0, cuts.at(2), margin);
EXPECT_NEAR(10.0, cuts.at(3), margin);
EXPECT_NEAR(13.0, cuts.at(4), margin);
auto labels = transform(X);
labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc4Q, X13BinsQuantile)
{
samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0 };
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(5, cuts.size());
EXPECT_NEAR(1.0, cuts.at(0), margin);
EXPECT_NEAR(4.0, cuts.at(1), margin);
EXPECT_NEAR(7.0, cuts.at(2), margin);
EXPECT_NEAR(10.0, cuts.at(3), margin);
EXPECT_NEAR(13.0, cuts.at(4), margin);
auto labels = transform(X);
labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc4U, X14BinsUniform)
{
samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0 };
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(5, cuts.size());
EXPECT_NEAR(1.0, cuts.at(0), margin);
EXPECT_NEAR(4.25, cuts.at(1), margin);
EXPECT_NEAR(7.5, cuts.at(2), margin);
EXPECT_NEAR(10.75, cuts.at(3), margin);
EXPECT_NEAR(14.0, cuts.at(4), margin);
auto labels = transform(X);
labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc4Q, X14BinsQuantile)
{
samples_t X = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0 };
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(5, cuts.size());
EXPECT_NEAR(1.0, cuts.at(0), margin);
EXPECT_NEAR(4.25, cuts.at(1), margin);
EXPECT_NEAR(7.5, cuts.at(2), margin);
EXPECT_NEAR(10.75, cuts.at(3), margin);
EXPECT_NEAR(14.0, cuts.at(4), margin);
auto labels = transform(X);
labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc4U, X15BinsUniform)
{
samples_t X = { 15.0, 8.0, 12.0, 14.0, 6.0, 1.0, 13.0, 11.0, 10.0, 9.0, 7.0, 4.0, 3.0, 5.0, 2.0 };
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(5, cuts.size());
EXPECT_NEAR(1.0, cuts.at(0), margin);
EXPECT_NEAR(4.5, cuts.at(1), margin);
EXPECT_NEAR(8, cuts.at(2), margin);
EXPECT_NEAR(11.5, cuts.at(3), margin);
EXPECT_NEAR(15.0, cuts.at(4), margin);
auto labels = transform(X);
labels_t expected = { 3, 1, 3, 3, 1, 0, 3, 2, 2, 2, 1, 0, 0, 1, 0 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc4Q, X15BinsQuantile)
{
samples_t X = { 15.0, 13.0, 12.0, 14.0, 6.0, 1.0, 8.0, 11.0, 10.0, 9.0, 7.0, 4.0, 3.0, 5.0, 2.0 };
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(5, cuts.size());
EXPECT_NEAR(1.0, cuts.at(0), margin);
EXPECT_NEAR(4.5, cuts.at(1), margin);
EXPECT_NEAR(8, cuts.at(2), margin);
EXPECT_NEAR(11.5, cuts.at(3), margin);
EXPECT_NEAR(15.0, cuts.at(4), margin);
auto labels = transform(X);
labels_t expected = { 3, 3, 3, 3, 1, 0, 1, 2, 2, 2, 1, 0, 0, 1, 0 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc4U, RepeatedValuesUniform)
{
samples_t X = { 0.0, 1.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0 };
// 0 1 2 3 4 5 6 7 8 9
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(5, cuts.size());
EXPECT_NEAR(0.0, cuts.at(0), margin);
EXPECT_NEAR(1.0, cuts.at(1), margin);
EXPECT_NEAR(2.0, cuts.at(2), margin);
EXPECT_NEAR(3.0, cuts.at(3), margin);
EXPECT_NEAR(4.0, cuts.at(4), margin);
auto labels = transform(X);
labels_t expected = { 0, 0, 0, 0, 1, 1, 2, 2, 2, 3 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc4Q, RepeatedValuesQuantile)
{
samples_t X = { 0.0, 1.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0 };
// 0 1 2 3 4 5 6 7 8 9
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(5, cuts.size());
EXPECT_NEAR(0.0, cuts.at(0), margin);
EXPECT_NEAR(1.0, cuts.at(1), margin);
EXPECT_NEAR(2.0, cuts.at(2), margin);
EXPECT_NEAR(3.0, cuts.at(3), margin);
EXPECT_NEAR(4.0, cuts.at(4), margin);
auto labels = transform(X);
labels_t expected = { 0, 0, 0, 0, 1, 1, 2, 2, 2, 3 };
EXPECT_EQ(expected, labels);
}
// TEST_F(TestBinDisc4U, irisUniform)
// {
// ArffFiles file;
// file.load(data_path + "iris.arff", true);
// vector<samples_t>& X = file.getX();
// fit(X[0]);
// auto Xt = transform(X[0]);
// labels_t expected = { 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 2, 2, 1, 2, 1, 2, 0, 2, 0, 0, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 0, 1, 1, 1, 2, 0, 1, 2, 1, 3, 2, 2, 3, 0, 3, 2, 3, 2, 2, 2, 1, 1, 2, 2, 3, 3, 1, 2, 1, 3, 2, 2, 3, 2, 1, 2, 3, 3, 3, 2, 2, 1, 3, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1 };
// EXPECT_EQ(expected, Xt);
// auto Xtt = fit_transform(X[0], file.getY());
// EXPECT_EQ(expected, Xtt);
// auto Xt_t = torch::tensor(X[0], torch::kFloat32);
// auto y_t = torch::tensor(file.getY(), torch::kInt32);
// auto Xtt_t = fit_transform_t(Xt_t, y_t);
// for (int i = 0; i < expected.size(); i++)
// EXPECT_EQ(expected[i], Xtt_t[i].item<int>());
// }
// TEST_F(TestBinDisc4Q, irisQuantile)
// {
// ArffFiles file;
// file.load(data_path + "iris.arff", true);
// vector<samples_t>& X = file.getX();
// fit(X[0]);
// auto Xt = transform(X[0]);
// labels_t expected = { 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 2, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 3, 3, 3, 1, 3, 1, 2, 0, 3, 1, 0, 2, 2, 2, 1, 3, 1, 2, 2, 1, 2, 2, 2, 2, 3, 3, 3, 3, 2, 1, 1, 1, 2, 2, 1, 2, 3, 2, 1, 1, 1, 2, 2, 0, 1, 1, 1, 2, 1, 1, 2, 2, 3, 2, 3, 3, 0, 3, 3, 3, 3, 3, 3, 1, 2, 3, 3, 3, 3, 2, 3, 1, 3, 2, 3, 3, 2, 2, 3, 3, 3, 3, 3, 2, 2, 3, 2, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 2, 2 };
// EXPECT_EQ(expected, Xt);
// auto Xtt = fit_transform(X[0], file.getY());
// EXPECT_EQ(expected, Xtt);
// auto Xt_t = torch::tensor(X[0], torch::kFloat32);
// auto y_t = torch::tensor(file.getY(), torch::kInt32);
// auto Xtt_t = fit_transform_t(Xt_t, y_t);
// for (int i = 0; i < expected.size(); i++)
// EXPECT_EQ(expected[i], Xtt_t[i].item<int>());
// fit_t(Xt_t, y_t);
// auto Xt_t2 = transform_t(Xt_t);
// for (int i = 0; i < expected.size(); i++)
// EXPECT_EQ(expected[i], Xt_t2[i].item<int>());
// }
TEST(TestBinDiscGeneric, Fileset)
{
Experiments exps(data_path + "tests.txt");
int num = 0;
while (exps.is_next()) {
Experiment exp = exps.next();
std::cout << "Exp #: " << ++num << " From: " << exp.from_ << " To: " << exp.to_ << " Step: " << exp.step_ << " Bins: " << exp.n_bins_ << " Strategy: " << exp.strategy_ << std::endl;
BinDisc disc(exp.n_bins_, exp.strategy_ == "Q" ? strategy_t::QUANTILE : strategy_t::UNIFORM);
std::vector<float> test;
for (float i = exp.from_; i < exp.to_; i += exp.step_) {
test.push_back(i);
}
// show_vector(test, "Test");
auto empty = std::vector<int>();
auto Xt = disc.fit_transform(test, empty);
auto cuts = disc.getCutPoints();
EXPECT_EQ(exp.discretized_data_.size(), Xt.size());
for (int i = 0; i < exp.discretized_data_.size(); ++i) {
if (exp.discretized_data_.at(i) != Xt.at(i)) {
std::cout << "Error at " << i << " Expected: " << exp.discretized_data_.at(i) << " Got: " << Xt.at(i) << std::endl;
}
}
EXPECT_EQ(exp.cutpoints_.size(), cuts.size());
for (int i = 0; i < exp.cutpoints_.size(); ++i) {
EXPECT_NEAR(exp.cutpoints_.at(i), cuts.at(i), margin);
}
}
}
}
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