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Fix BinDisc quantile mistakes

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This commit is contained in:
Ricardo Montañana Gómez
2024-07-02 09:40:06 +02:00
parent 7b0673fd4b
commit 8f6e16f04f
16 changed files with 618 additions and 157 deletions
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316
tests/BinDisc_unittest.cpp
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@@ -4,6 +4,7 @@
#include "gtest/gtest.h"
#include "ArffFiles.h"
#include "../BinDisc.h"
#include "Experiments.hpp"
namespace mdlp {
const float margin = 1e-4;
@@ -40,10 +41,11 @@ namespace mdlp {
auto y = labels_t();
fit(X, y);
auto cuts = getCutPoints();
ASSERT_EQ(3, cuts.size());
EXPECT_NEAR(3.66667, cuts.at(0), margin);
EXPECT_NEAR(6.33333, cuts.at(1), margin);
EXPECT_EQ(numeric_limits<float>::max(), cuts.at(2));
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);
@@ -53,10 +55,11 @@ namespace mdlp {
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(3, cuts.size());
EXPECT_NEAR(3.666667, cuts[0], margin);
EXPECT_NEAR(6.333333, cuts[1], margin);
EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
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);
@@ -66,12 +69,13 @@ namespace mdlp {
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(3, cuts.size());
EXPECT_EQ(4.0, cuts[0]);
EXPECT_EQ(7.0, cuts[1]);
EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
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, 1, 1, 1, 2, 2, 2, 2 };
labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc3Q, X10BinsQuantile)
@@ -79,12 +83,13 @@ namespace mdlp {
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(3, cuts.size());
EXPECT_EQ(4, cuts[0]);
EXPECT_EQ(7, cuts[1]);
EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
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, 1, 1, 1, 2, 2, 2, 2 };
labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc3U, X11BinsUniform)
@@ -92,10 +97,11 @@ namespace mdlp {
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(3, cuts.size());
EXPECT_NEAR(4.33333, cuts[0], margin);
EXPECT_NEAR(7.66667, cuts[1], margin);
EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
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);
@@ -105,10 +111,11 @@ namespace mdlp {
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(3, cuts.size());
EXPECT_NEAR(4.33333, cuts[0], margin);
EXPECT_NEAR(7.66667, cuts[1], margin);
EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
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);
@@ -118,8 +125,9 @@ namespace mdlp {
samples_t X = { 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(1, cuts.size());
EXPECT_EQ(numeric_limits<float>::max(), cuts[0]);
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);
@@ -129,8 +137,9 @@ namespace mdlp {
samples_t X = { 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
fit(X);
auto cuts = getCutPoints();
EXPECT_EQ(1, cuts.size());
EXPECT_EQ(numeric_limits<float>::max(), cuts[0]);
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);
@@ -140,16 +149,18 @@ namespace mdlp {
samples_t X = {};
fit(X);
auto cuts = getCutPoints();
EXPECT_EQ(1, cuts.size());
EXPECT_EQ(numeric_limits<float>::max(), cuts[0]);
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();
EXPECT_EQ(1, cuts.size());
EXPECT_EQ(numeric_limits<float>::max(), cuts[0]);
ASSERT_EQ(2, cuts.size());
EXPECT_NEAR(0, cuts.at(0), margin);
EXPECT_NEAR(0, cuts.at(1), margin);
}
TEST(TestBinDisc3, ExceptionNumberBins)
{
@@ -160,10 +171,11 @@ namespace mdlp {
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(1.66667, cuts[0], margin);
EXPECT_NEAR(2.33333, cuts[1], margin);
EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
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);
@@ -174,9 +186,10 @@ namespace mdlp {
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();
EXPECT_EQ(2, cuts.size());
EXPECT_NEAR(1.66667, cuts[0], margin);
EXPECT_EQ(numeric_limits<float>::max(), cuts[1]);
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);
@@ -187,11 +200,12 @@ namespace mdlp {
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();
EXPECT_EQ(4, cuts.size());
ASSERT_EQ(3.75, cuts[0]);
EXPECT_EQ(6.5, cuts[1]);
EXPECT_EQ(9.25, cuts[2]);
EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
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);
@@ -201,11 +215,12 @@ namespace mdlp {
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();
EXPECT_EQ(4, cuts.size());
ASSERT_EQ(3.75, cuts[0]);
EXPECT_EQ(6.5, cuts[1]);
EXPECT_EQ(9.25, cuts[2]);
EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
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);
@@ -215,13 +230,14 @@ namespace mdlp {
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();
EXPECT_EQ(4, cuts.size());
EXPECT_EQ(4.0, cuts[0]);
EXPECT_EQ(7.0, cuts[1]);
EXPECT_EQ(10.0, cuts[2]);
EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
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, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3 };
labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc4Q, X13BinsQuantile)
@@ -229,13 +245,14 @@ namespace mdlp {
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();
EXPECT_EQ(4, cuts.size());
EXPECT_EQ(4.0, cuts[0]);
EXPECT_EQ(7.0, cuts[1]);
EXPECT_EQ(10.0, cuts[2]);
EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
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, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3 };
labels_t expected = { 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc4U, X14BinsUniform)
@@ -243,11 +260,12 @@ namespace mdlp {
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();
EXPECT_EQ(4, cuts.size());
EXPECT_EQ(4.25, cuts[0]);
EXPECT_EQ(7.5, cuts[1]);
EXPECT_EQ(10.75, cuts[2]);
EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
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);
@@ -257,11 +275,12 @@ namespace mdlp {
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();
EXPECT_EQ(4, cuts.size());
EXPECT_EQ(4.25, cuts[0]);
EXPECT_EQ(7.5, cuts[1]);
EXPECT_EQ(10.75, cuts[2]);
EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
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);
@@ -271,13 +290,14 @@ namespace mdlp {
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();
EXPECT_EQ(4, cuts.size());
EXPECT_EQ(4.5, cuts[0]);
EXPECT_EQ(8, cuts[1]);
EXPECT_EQ(11.5, cuts[2]);
EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
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, 2, 3, 3, 1, 0, 3, 2, 2, 2, 1, 0, 0, 1, 0 };
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)
@@ -285,13 +305,14 @@ namespace mdlp {
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();
EXPECT_EQ(4, cuts.size());
EXPECT_EQ(4.5, cuts[0]);
EXPECT_EQ(8, cuts[1]);
EXPECT_EQ(11.5, cuts[2]);
EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
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, 2, 2, 2, 2, 1, 0, 0, 1, 0 };
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)
@@ -300,13 +321,14 @@ namespace mdlp {
// 0 1 2 3 4 5 6 7 8 9
fit(X);
auto cuts = getCutPoints();
EXPECT_EQ(4, cuts.size());
EXPECT_EQ(1.0, cuts[0]);
EXPECT_EQ(2.0, cuts[1]);
ASSERT_EQ(3.0, cuts[2]);
EXPECT_EQ(numeric_limits<float>::max(), cuts[3]);
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, 1, 1, 1, 2, 2, 3, 3, 3, 3 };
labels_t expected = { 0, 0, 0, 0, 1, 1, 2, 2, 2, 3 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc4Q, RepeatedValuesQuantile)
@@ -315,50 +337,80 @@ namespace mdlp {
// 0 1 2 3 4 5 6 7 8 9
fit(X);
auto cuts = getCutPoints();
ASSERT_EQ(3, cuts.size());
EXPECT_EQ(2.0, cuts[0]);
ASSERT_EQ(3.0, cuts[1]);
EXPECT_EQ(numeric_limits<float>::max(), cuts[2]);
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, 2 };
labels_t expected = { 0, 0, 0, 0, 1, 1, 2, 2, 2, 3 };
EXPECT_EQ(expected, labels);
}
TEST_F(TestBinDisc4U, irisUniform)
// 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)
{
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>());
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);
}
}
}
}

9
tests/Discretizer_unittest.cpp
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@@ -21,6 +21,15 @@ namespace mdlp {
}
const std::string data_path = set_data_path();
TEST(Discretizer, Version)
{
Discretizer* disc = new BinDisc(4, strategy_t::UNIFORM);
auto version = disc->version();
delete disc;
std::cout << "Version computed: " << version;
EXPECT_EQ("1.2.3", version);
}
TEST(Discretizer, BinIrisUniform)
{
ArffFiles file;

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tests/Experiments.hpp Normal file
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#ifndef EXPERIMENTS_HPP
#define EXPERIMENTS_HPP
#include<sstream>
#include<iostream>
#include<string>
#include<fstream>
#include<vector>
#include<tuple>
#include "../typesFImdlp.h"
class Experiment {
public:
Experiment(float from_, float to_, float step_, int n_bins, std::string strategy, std::vector<int> data_discretized, std::vector<float> cutpoints) :
from_{ from_ }, to_{ to_ }, step_{ step_ }, n_bins_{ n_bins }, strategy_{ strategy }, discretized_data_{ data_discretized }, cutpoints_{ cutpoints }
{
if (strategy != "Q" && strategy != "U") {
throw std::invalid_argument("Invalid strategy " + strategy);
}
}
float from_;
float to_;
float step_;
int n_bins_;
std::string strategy_;
std::vector<int> discretized_data_;
std::vector<float> cutpoints_;
};
class Experiments {
public:
Experiments(const std::string filename) : filename{ filename }
{
test_file.open(filename);
if (!test_file.is_open()) {
throw std::runtime_error("File " + filename + " not found");
}
exp_end = false;
}
~Experiments()
{
test_file.close();
}
bool end() const
{
return exp_end;
}
bool is_next()
{
while (std::getline(test_file, line) && line[0] == '#');
if (test_file.eof()) {
exp_end = true;
return false;
}
return true;
}
Experiment next()
{
return parse_experiment(line);
}
private:
std::tuple<float, float, float, int, std::string> parse_header(const std::string& line)
{
std::istringstream iss(line);
std::string from_, to_, step_, n_bins, strategy;
iss >> from_ >> to_ >> step_ >> n_bins >> strategy;
return { std::stof(from_), std::stof(to_), std::stof(step_), std::stoi(n_bins), strategy };
}
template <typename T>
std::vector<T> parse_vector(const std::string& line)
{
std::istringstream iss(line);
std::vector<T> data;
std::string d;
while (iss >> d) {
data.push_back(std::is_same<T, float>::value ? std::stof(d) : std::stoi(d));
}
return data;
}
Experiment parse_experiment(std::string& line)
{
auto [from_, to_, step_, n_bins, strategy] = parse_header(line);
std::getline(test_file, line);
auto data_discretized = parse_vector<int>(line);
std::getline(test_file, line);
auto cutpoints = parse_vector<float>(line);
return Experiment{ from_, to_, step_, n_bins, strategy, data_discretized, cutpoints };
}
std::ifstream test_file;
std::string filename;
std::string line;
bool exp_end;
};
template <typename T>
void show_vector(const std::vector<T>& data, std::string title)
{
std::cout << title << ": ";
std::string sep = "";
for (const auto& d : data) {
std::cout << sep << d;
sep = ", ";
}
std::cout << std::endl;
}
#endif

35
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@@ -0,0 +1,35 @@
#
# from, to, step, #bins, Q/U
# discretized data
# cut points
#
0, 100, 1, 4, Q
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3
0.0, 24.75, 49.5, 74.25, 99.0
0, 50, 1, 4, Q
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3
0.0, 12.25, 24.5, 36.75, 49.0
0, 100, 1, 3, Q
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
0.0, 33.0, 66.0, 99.0
0, 50, 1, 3, Q
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
0.0, 16.33333, 32.66667, 49.0
0, 10, 1, 3, Q
0, 0, 0, 0, 1, 1, 1, 2, 2, 2
0.0, 3.0, 6.0, 9.0
0, 100, 1, 4, U
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3
0.0, 24.75, 49.5, 74.25, 99.0
0, 50, 1, 4, U
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3
0.0, 12.25, 24.5, 36.75, 49.0
0, 100, 1, 3, U
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
0.0, 33.0, 66.0, 99.0
0, 50, 1, 3, U
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
0.0, 16.33333, 32.66667, 49.0
0, 10, 1, 3, U
0, 0, 0, 1, 1, 1, 2, 2, 2, 2
0.0, 3.0, 6.0, 9.0

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@@ -0,0 +1,32 @@
#include <iostream>
#include <vector>
#include <algorithm> // For std::lower_bound
std::vector<int> searchsorted(const std::vector<float>& cuts, const std::vector<float>& data) {
std::vector<int> indices;
indices.reserve(data.size());
for (const float& value : data) {
// Find the first position in 'a' where 'value' could be inserted to maintain order
auto it = std::lower_bound(cuts.begin(), cuts.end(), value);
// Calculate the index
int index = it - cuts.begin();
indices.push_back(index);
}
return indices;
}
int main() {
std::vector<float> cuts = { 10.0 };
std::vector<float> data = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0 };
std::vector<int> result = searchsorted(cuts, data);
for (int idx : result) {
std::cout << idx << " ";
}
return 0;
}

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102
tests/t.cpp Normal file
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@@ -0,0 +1,102 @@
#include <iostream>
#include <algorithm>
#include <cmath>
#include <vector>
#include <string>
typedef float precision_t;
std::vector<int> transform(const std::vector<float> cutPoints, const std::vector<float>& data)
{
std::vector<int> discretizedData;
discretizedData.reserve(data.size());
for (const float& item : data) {
auto upper = std::lower_bound(cutPoints.begin(), cutPoints.end(), item);
discretizedData.push_back(upper - cutPoints.begin());
}
return discretizedData;
}
template <typename T>
void show_vector(const std::vector<T>& data, std::string title)
{
std::cout << title << ": ";
std::string sep = "";
for (const auto& d : data) {
std::cout << sep << d;
sep = ", ";
}
std::cout << std::endl;
}
std::vector<precision_t> linspace(precision_t start, precision_t end, int num)
{
if (start == end) {
return { start, end };
}
precision_t delta = (end - start) / static_cast<precision_t>(num - 1);
std::vector<precision_t> linspc;
for (size_t i = 0; i < num - 1; ++i) {
precision_t val = start + delta * static_cast<precision_t>(i);
linspc.push_back(val);
}
return linspc;
}
size_t clip(const size_t n, size_t lower, size_t upper)
{
return std::max(lower, std::min(n, upper));
}
std::vector<precision_t> percentile(std::vector<precision_t>& data, std::vector<precision_t>& percentiles)
{
// Implementation taken from https://dpilger26.github.io/NumCpp/doxygen/html/percentile_8hpp_source.html
std::vector<precision_t> results;
results.reserve(percentiles.size());
for (auto percentile : percentiles) {
const size_t i = static_cast<size_t>(std::floor(static_cast<double>(data.size() - 1) * percentile / 100.));
const auto indexLower = clip(i, 0, data.size() - 2);
const double percentI = static_cast<double>(indexLower) / static_cast<double>(data.size() - 1);
const double fraction =
(percentile / 100.0 - percentI) /
(static_cast<double>(indexLower + 1) / static_cast<double>(data.size() - 1) - percentI);
const auto value = data[indexLower] + (data[indexLower + 1] - data[indexLower]) * fraction;
if (value != results.back())
results.push_back(value);
}
return results;
}
int main()
{
// std::vector<float> test;
// std::vector<float> cuts = { 0, 24.75, 49.5, 74.25, 10000 };
// for (int i = 0; i < 100; ++i) {
// test.push_back(i);
// }
// auto Xt = transform(cuts, test);
// show_vector(Xt, "Discretized data:");
// std::vector<float> test2 = { 0,1,2,3,4,5,6,7,8,9,10,11 };
// std::vector<float> cuts2 = { 0,1,2,3,4,5,6,7,8,9 };
// auto Xt2 = transform(cuts2, test2);
// show_vector(Xt2, "discretized data2: ");
auto quantiles = linspace(0.0, 100.0, 3 + 1);
std::vector<float> data = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 };
std::vector<float> cutPoints;
std::sort(data.begin(), data.end());
cutPoints = percentile(data, quantiles);
cutPoints.push_back(std::numeric_limits<precision_t>::max());
data.push_back(15);
data.push_back(0);
cutPoints.pop_back();
cutPoints.erase(cutPoints.begin());
cutPoints.clear();
cutPoints.push_back(9.0);
auto Xt = transform(cutPoints, data);
show_vector(data, "Original data");
show_vector(Xt, "Discretized data");
show_vector(cutPoints, "Cutpoints");
return 0;
}
/*
n_bins = 3
data = [1,2,3,4,5,6,7,8,9,10]
quantiles = np.linspace(0, 100, n_bins + 1)
bin_edges = np.percentile(data, quantiles)
*/

39
tests/tests_do.py Normal file
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@@ -0,0 +1,39 @@
from sklearn.preprocessing import KBinsDiscretizer
with open("datasets/tests.txt") as f:
data = f.readlines()
data = [x.strip() for x in data if x[0] != "#"]
for i in range(0, len(data), 3):
print("Experiment:", data[i])
from_, to_, step_, n_bins_, strategy_ = data[i].split(",")
strategy = "quantile" if strategy_.strip() == "Q" else "uniform"
disc = KBinsDiscretizer(
n_bins=int(n_bins_),
encode="ordinal",
strategy=strategy,
)
X = [[float(x)] for x in range(int(from_), int(to_), int(step_))]
# result = disc.fit_transform(X)
disc.fit(X)
result = disc.transform(X)
result = [int(x) for x in result.flatten()]
expected = [int(x) for x in data[i + 1].split(",")]
assert len(result) == len(expected)
for j in range(len(result)):
if result[j] != expected[j]:
print("Error at", j, "Expected=", expected[j], "Result=", result[j])
expected_cuts = disc.bin_edges_[0]
computed_cuts = [float(x) for x in data[i + 2].split(",")]
assert len(expected_cuts) == len(computed_cuts)
for j in range(len(expected_cuts)):
if round(expected_cuts[j], 5) != computed_cuts[j]:
print(
"Error at",
j,
"Expected=",
expected_cuts[j],
"Result=",
computed_cuts[j],
)

85
tests/tests_generate.ipynb Normal file
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@@ -0,0 +1,85 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import KBinsDiscretizer"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"experiments = [\n",
" [0, 100, 1, 4, \"Q\"],\n",
" [0, 50, 1, 4, \"Q\"],\n",
" [0, 100, 1, 3, \"Q\"],\n",
" [0, 50, 1, 3, \"Q\"],\n",
" [0, 10, 1, 3, \"Q\"],\n",
" [0, 100, 1, 4, \"U\"],\n",
" [0, 50, 1, 4, \"U\"],\n",
" [0, 100, 1, 3, \"U\"],\n",
" [0, 50, 1, 3, \"U\"],\n",
" [0, 10, 1, 3, \"U\"],\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"with open(\"datasets/tests.txt\", \"w\") as file:\n",
" file.write(\"#\\n\")\n",
" file.write(\"# from, to, step, #bins, Q/U\\n\")\n",
" file.write(\"# discretized data\\n\")\n",
" file.write(\"# cut points\\n\")\n",
" file.write(\"#\\n\")\n",
" for experiment in experiments:\n",
" (from_, to_, step_, bins_, strategy) = experiment\n",
" disc = KBinsDiscretizer(n_bins=bins_, encode='ordinal', strategy='quantile' if strategy.strip() == \"Q\" else 'uniform')\n",
" data = [[x] for x in range(from_, to_, step_)]\n",
" disc.fit(data)\n",
" result = disc.transform(data)\n",
" file.write(f\"{from_}, {to_}, {step_}, {bins_}, {strategy}\\n\")\n",
" sep = \"\"\n",
" for res in result:\n",
" file.write(f\"{sep}{int(res):d}\")\n",
" sep= \", \"\n",
" file.write(\"\\n\")\n",
" sep = \"\"\n",
" for res in disc.bin_edges_[0]:\n",
" file.write(sep + str(round(res,5)))\n",
" sep = \", \"\n",
" file.write(\"\\n\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}