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Add max_depth and min_length as hyperparams

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This commit is contained in:
Ricardo Montañana Gómez
2023-02-25 18:16:20 +01:00
parent e25ca378f0
commit d6cece1006
4 changed files with 105 additions and 36 deletions
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28
CPPFImdlp.cpp
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@@ -8,7 +8,11 @@
namespace mdlp {
CPPFImdlp::CPPFImdlp(): indices(indices_t()), X(samples_t()), y(labels_t()),
CPPFImdlp::CPPFImdlp():depth(0), max_depth(numeric_limits<int>::max()), min_length(3), indices(indices_t()), X(samples_t()), y(labels_t()),
metrics(Metrics(y, indices))
{
}
CPPFImdlp::CPPFImdlp(int min_length_, int max_depth_): depth(0), max_depth(max_depth_), min_length(min_length_), indices(indices_t()), X(samples_t()), y(labels_t()),
metrics(Metrics(y, indices))
{
}
@@ -25,9 +29,15 @@ namespace mdlp {
if (X.empty() || y.empty()) {
throw invalid_argument("X and y must have at least one element");
}
if (min_length < 3) {
throw invalid_argument("min_length must be greater than 2");
}
if (max_depth < 1) {
throw invalid_argument("max_depth must be greater than 0");
}
indices = sortIndices(X_, y_);
metrics.setData(y, indices);
computeCutPoints(0, X.size());
computeCutPoints(0, X.size(), 1);
return *this;
}
@@ -60,12 +70,14 @@ namespace mdlp {
return { (actual + previous) / 2, cut };
}
void CPPFImdlp::computeCutPoints(size_t start, size_t end)
void CPPFImdlp::computeCutPoints(size_t start, size_t end, int depth_)
{
size_t cut;
pair<precision_t, size_t> result;
if (end - start < 3)
// Check if the interval length and the depth are Ok
if (end - start < min_length || depth_ > max_depth)
return;
depth = depth_ > depth ? depth_ : depth;
cut = getCandidate(start, end);
if (cut == numeric_limits<size_t>::max())
return;
@@ -73,8 +85,8 @@ namespace mdlp {
result = valueCutPoint(start, cut, end);
cut = result.second;
cutPoints.push_back(result.first);
computeCutPoints(start, cut);
computeCutPoints(cut, end);
computeCutPoints(start, cut, depth_ + 1);
computeCutPoints(cut, end, depth_ + 1);
}
}
@@ -158,4 +170,8 @@ namespace mdlp {
sort(output.begin(), output.end());
return output;
}
int CPPFImdlp::get_depth()
{
return depth;
}
}

8
CPPFImdlp.h
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@@ -10,19 +10,23 @@ namespace mdlp {
indices_t indices;
samples_t X;
labels_t y;
int depth, max_depth;
size_t min_length;
Metrics metrics;
cutPoints_t cutPoints;
static indices_t sortIndices(samples_t&, labels_t&);
void computeCutPoints(size_t, size_t);
void computeCutPoints(size_t, size_t, int);
bool mdlp(size_t, size_t, size_t);
size_t getCandidate(size_t, size_t);
pair<precision_t, size_t> valueCutPoint(size_t, size_t, size_t);
public:
CPPFImdlp();
CPPFImdlp(int, int);
~CPPFImdlp();
CPPFImdlp& fit(samples_t&, labels_t&);
samples_t getCutPoints();
cutPoints_t getCutPoints();
int get_depth();
inline string version() { return "1.1.1"; };
};
}

103
tests/FImdlp_unittest.cpp
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@@ -8,6 +8,7 @@ namespace mdlp {
class TestFImdlp: public CPPFImdlp, public testing::Test {
public:
precision_t precision = 0.000001;
//precision_t precision = 0.000000000001;
TestFImdlp(): CPPFImdlp() {}
void SetUp()
{
@@ -25,18 +26,16 @@ namespace mdlp {
prev = X[testSortedIndices[i]];
}
}
void checkCutPoints(cutPoints_t& expected)
void checkCutPoints(cutPoints_t& computed, cutPoints_t& expected)
{
int expectedSize = expected.size();
EXPECT_EQ(cutPoints.size(), expectedSize);
for (unsigned long i = 0; i < cutPoints.size(); i++) {
EXPECT_NEAR(cutPoints[i], expected[i], precision);
EXPECT_EQ(computed.size(), expected.size());
for (unsigned long i = 0; i < computed.size(); i++) {
EXPECT_NEAR(computed[i], expected[i], precision);
}
}
template<typename T, typename A>
void checkVectors(std::vector<T, A> const& expected, std::vector<T, A> const& computed)
{
EXPECT_EQ(expected.size(), computed.size());
ASSERT_EQ(expected.size(), computed.size());
for (auto i = 0; i < expected.size(); i++) {
EXPECT_NEAR(expected[i], computed[i], precision);
@@ -55,6 +54,20 @@ namespace mdlp {
EXPECT_EQ(result.second, limit);
return true;
}
void test_dataset(CPPFImdlp& test, string filename, vector<cutPoints_t>& expected, int depths[])
{
ArffFiles file;
file.load("../datasets/" + filename, true);
vector<samples_t>& X = file.getX();
labels_t& y = file.getY();
auto attributes = file.getAttributes();
for (auto feature = 0; feature < attributes.size(); feature++) {
test.fit(X[feature], y);
EXPECT_EQ(test.get_depth(), depths[feature]);
auto computed = test.getCutPoints();
checkCutPoints(computed, expected[feature]);
}
}
};
TEST_F(TestFImdlp, FitErrorEmptyDataset)
{
@@ -68,6 +81,15 @@ namespace mdlp {
y = { 1, 2 };
EXPECT_THROW(fit(X, y), std::invalid_argument);
}
TEST_F(TestFImdlp, FitErrorMinLengtMaxDepth)
{
auto testLength = CPPFImdlp(2, 10);
auto testDepth = CPPFImdlp(3, 0);
X = { 1, 2, 3 };
y = { 1, 2, 3 };
EXPECT_THROW(testLength.fit(X, y), invalid_argument);
EXPECT_THROW(testDepth.fit(X, y), invalid_argument);
}
TEST_F(TestFImdlp, SortIndices)
{
X = { 5.7, 5.3, 5.2, 5.1, 5.0, 5.6, 5.1, 6.0, 5.1, 5.9 };
@@ -114,7 +136,7 @@ namespace mdlp {
TEST_F(TestFImdlp, TestArtificialDataset)
{
fit(X, y);
computeCutPoints(0, 20);
computeCutPoints(0, 20, 1);
cutPoints_t expected = { 5.05 };
vector<precision_t> computed = getCutPoints();
computed = getCutPoints();
@@ -126,28 +148,15 @@ namespace mdlp {
}
TEST_F(TestFImdlp, TestIris)
{
ArffFiles file;
string path = "../datasets/";
file.load(path + "iris.arff", true);
int items = file.getSize();
vector<samples_t>& X = file.getX();
vector<cutPoints_t> expected = {
{ 5.4499998092651367, 5.75 },
{ 5.45, 5.75 },
{ 2.75, 2.85, 2.95, 3.05, 3.35 },
{ 2.4500000476837158, 4.75, 5.0500001907348633 },
{ 0.80000001192092896, 1.75 }
{ 2.45, 4.75, 5.05 },
{ 0.8, 1.75 }
};
labels_t& y = file.getY();
auto attributes = file.getAttributes();
for (auto feature = 0; feature < attributes.size(); feature++) {
fit(X[feature], y);
vector<precision_t> computed = getCutPoints();
EXPECT_EQ(computed.size(), expected[feature].size());
for (auto i = 0; i < computed.size(); i++) {
EXPECT_NEAR(computed[i], expected[feature][i], precision);
}
}
int depths[] = { 3, 5, 5, 5 };
auto test = CPPFImdlp();
test_dataset(test, "iris.arff", expected, depths);
}
TEST_F(TestFImdlp, ComputeCutPointsGCase)
{
@@ -156,7 +165,8 @@ namespace mdlp {
samples_t X_ = { 0, 1, 2, 2, 2 };
labels_t y_ = { 1, 1, 1, 2, 2 };
fit(X_, y_);
checkCutPoints(expected);
auto computed = getCutPoints();
checkCutPoints(computed, expected);
}
TEST_F(TestFImdlp, ValueCutPoint)
{
@@ -178,4 +188,43 @@ namespace mdlp {
samples_t X4c = { 3.1, 3.2, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7 };
test_result(X4c, 4, 6.9 / 2, 2, "4c");
}
TEST_F(TestFImdlp, MaxDepth)
{
// Set max_depth to 2
auto test = CPPFImdlp(3, 1);
vector<cutPoints_t> expected = {
{ 5.45 },
{ 3.35 },
{ 2.45 },
{0.8 }
};
int depths[] = { 1, 1, 1, 1 };
test_dataset(test, "iris.arff", expected, depths);
}
TEST_F(TestFImdlp, MinLength)
{
// Set min_length to 75
auto test = CPPFImdlp(75, 100);
vector<cutPoints_t> expected = {
{ 5.45, 5.75 },
{ 2.85, 3.35 },
{ 2.45, 4.75 },
{ 0.8, 1.75 }
};
int depths[] = { 3, 3, 3, 3 };
test_dataset(test, "iris.arff", expected, depths);
}
TEST_F(TestFImdlp, MinLengthMaxDepth)
{
// Set min_length to 75
auto test = CPPFImdlp(75, 2);
vector<cutPoints_t> expected = {
{ 5.45, 5.75 },
{ 2.85, 3.35 },
{ 2.45, 4.75 },
{ 0.8, 1.75 }
};
int depths[] = { 2, 2, 2, 2 };
test_dataset(test, "iris.arff", expected, depths);
}
}

2
tests/test
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@@ -9,4 +9,4 @@ if test $? -ne 0; then
exit 1
fi
cd build
ctest --output-on-failure
ctest --output-on-failure|grep -v profiling