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Add predict_proba to Ld classifiers

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Ricardo Montañana Gómez
2025-05-12 19:47:04 +02:00
parent 8a02a3a5cb
commit b11620bbe8
12 changed files with 116 additions and 44 deletions
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1
CHANGELOG.md
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@@ -13,6 +13,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
- Fix the vcpkg configuration in building the library.
- Fix the sample app to use the vcpkg configuration.
- Add predict_proba method to all Ld classifiers.
## [1.1.0] - 2025-04-27

1
README.md
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@@ -6,6 +6,7 @@
[![Codacy Badge](https://app.codacy.com/project/badge/Grade/cf3e0ac71d764650b1bf4d8d00d303b1)](https://app.codacy.com/gh/Doctorado-ML/BayesNet/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
[![Security Rating](https://sonarcloud.io/api/project_badges/measure?project=rmontanana_BayesNet&metric=security_rating)](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
[![Reliability Rating](https://sonarcloud.io/api/project_badges/measure?project=rmontanana_BayesNet&metric=reliability_rating)](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/Doctorado-ML/BayesNet)
![Gitea Last Commit](https://img.shields.io/gitea/last-commit/rmontanana/bayesnet?gitea_url=https://gitea.rmontanana.es&logo=gitea)
[![Coverage Badge](https://img.shields.io/badge/Coverage-99,1%25-green)](https://gitea.rmontanana.es/rmontanana/BayesNet)
[![DOI](https://zenodo.org/badge/667782806.svg)](https://doi.org/10.5281/zenodo.14210344)

5
bayesnet/classifiers/KDBLd.cc
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@@ -28,6 +28,11 @@ namespace bayesnet {
auto Xt = prepareX(X);
return KDB::predict(Xt);
}
torch::Tensor KDBLd::predict_proba(torch::Tensor& X)
{
auto Xt = prepareX(X);
return KDB::predict_proba(Xt);
}
std::vector<std::string> KDBLd::graph(const std::string& name) const
{
return KDB::graph(name);

1
bayesnet/classifiers/KDBLd.h
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@@ -18,6 +18,7 @@ namespace bayesnet {
KDBLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
std::vector<std::string> graph(const std::string& name = "KDB") const override;
torch::Tensor predict(torch::Tensor& X) override;
torch::Tensor predict_proba(torch::Tensor& X) override;
static inline std::string version() { return "0.0.1"; };
};
}

2
bayesnet/classifiers/Proposal.cc
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@@ -11,7 +11,7 @@ namespace bayesnet {
Proposal::~Proposal()
{
for (auto& [key, value] : discretizers) {
delete value;
delete value;
}
}
void Proposal::checkInput(const torch::Tensor& X, const torch::Tensor& y)

5
bayesnet/classifiers/SPODELd.cc
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@@ -43,6 +43,11 @@ namespace bayesnet {
auto Xt = prepareX(X);
return SPODE::predict(Xt);
}
torch::Tensor SPODELd::predict_proba(torch::Tensor& X)
{
auto Xt = prepareX(X);
return SPODE::predict_proba(Xt);
}
std::vector<std::string> SPODELd::graph(const std::string& name) const
{
return SPODE::graph(name);

1
bayesnet/classifiers/SPODELd.h
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@@ -19,6 +19,7 @@ namespace bayesnet {
SPODELd& commonFit(const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing);
std::vector<std::string> graph(const std::string& name = "SPODELd") const override;
torch::Tensor predict(torch::Tensor& X) override;
torch::Tensor predict_proba(torch::Tensor& X) override;
static inline std::string version() { return "0.0.1"; };
};
}

5
bayesnet/classifiers/TANLd.cc
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@@ -29,6 +29,11 @@ namespace bayesnet {
auto Xt = prepareX(X);
return TAN::predict(Xt);
}
torch::Tensor TANLd::predict_proba(torch::Tensor& X)
{
auto Xt = prepareX(X);
return TAN::predict_proba(Xt);
}
std::vector<std::string> TANLd::graph(const std::string& name) const
{
return TAN::graph(name);

1
bayesnet/classifiers/TANLd.h
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@@ -18,6 +18,7 @@ namespace bayesnet {
TANLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) override;
std::vector<std::string> graph(const std::string& name = "TANLd") const override;
torch::Tensor predict(torch::Tensor& X) override;
torch::Tensor predict_proba(torch::Tensor& X) override;
};
}
#endif // !TANLD_H

22
sample/CMakeLists.txt
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@@ -10,13 +10,31 @@ find_package(Torch CONFIG REQUIRED)
find_package(fimdlp CONFIG REQUIRED)
find_package(folding CONFIG REQUIRED)
find_package(arff-files CONFIG REQUIRED)
find_package(bayesnet CONFIG REQUIRED)
find_package(nlohmann_json CONFIG REQUIRED)
option(BAYESNET_VCPKG_CONFIG "Use vcpkg config for BayesNet" ON)
if (BAYESNET_VCPKG_CONFIG)
message(STATUS "Using BayesNet vcpkg config")
find_package(bayesnet CONFIG REQUIRED)
set(BayesNet_LIBRARIES bayesnet::bayesnet)
else(BAYESNET_VCPKG_CONFIG)
message(STATUS "Using BayesNet local library config")
find_library(bayesnet NAMES libbayesnet bayesnet libbayesnet.a PATHS ${Platform_SOURCE_DIR}/../lib/lib REQUIRED)
find_path(Bayesnet_INCLUDE_DIRS REQUIRED NAMES bayesnet PATHS ${Platform_SOURCE_DIR}/../lib/include)
add_library(bayesnet::bayesnet UNKNOWN IMPORTED)
set_target_properties(bayesnet::bayesnet PROPERTIES
IMPORTED_LOCATION ${bayesnet}
INTERFACE_INCLUDE_DIRECTORIES ${Bayesnet_INCLUDE_DIRS}
)
endif(BAYESNET_VCPKG_CONFIG)
message(STATUS "BayesNet: ${bayesnet}")
add_executable(bayesnet_sample sample.cc)
target_link_libraries(bayesnet_sample PRIVATE
fimdlp::fimdlp
arff-files::arff-files
"${TORCH_LIBRARIES}"
bayesnet::bayesnet
bayesnet::bayesnet
folding::folding
)

114
sample/sample.cc
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@@ -4,9 +4,22 @@
// SPDX-License-Identifier: MIT
// ***************************************************************
#include <map>
#include <string>
#include <ArffFiles/ArffFiles.hpp>
#include <fimdlp/CPPFImdlp.h>
#include <bayesnet/ensembles/XBAODE.h>
#include <bayesnet/classifiers/TANLd.h>
#include <bayesnet/classifiers/KDBLd.h>
#include <bayesnet/ensembles/AODELd.h>
torch::Tensor matrix2tensor(const std::vector<std::vector<float>>& matrix)
{
auto tensor = torch::empty({ static_cast<int>(matrix.size()), static_cast<int>(matrix[0].size()) }, torch::kFloat32);
for (int i = 0; i < matrix.size(); ++i) {
tensor.index_put_({ i, "..." }, torch::tensor(matrix[i], torch::kFloat32));
}
return tensor;
}
std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y)
{
@@ -19,32 +32,40 @@ std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, m
}
return Xd;
}
tuple<torch::Tensor, torch::Tensor, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::string& name, bool class_last)
std::tuple<torch::Tensor, torch::Tensor, std::vector<std::string>, std::string> loadArff(const std::string& name, bool class_last)
{
auto handler = ArffFiles();
handler.load(name, class_last);
// Get Dataset X, y
std::vector<mdlp::samples_t>& X = handler.getX();
mdlp::labels_t& y = handler.getY();
// Get className & Features
auto className = handler.getClassName();
std::vector<mdlp::samples_t> X = handler.getX();
mdlp::labels_t y = handler.getY();
std::vector<std::string> features;
auto attributes = handler.getAttributes();
transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
torch::Tensor Xd;
auto states = map<std::string, std::vector<int>>();
auto Xr = discretizeDataset(X, y);
Xd = torch::zeros({ static_cast<int>(Xr.size()), static_cast<int>(Xr[0].size()) }, torch::kInt32);
for (int i = 0; i < features.size(); ++i) {
states[features[i]] = std::vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
auto item = states.at(features[i]);
iota(begin(item), end(item), 0);
Xd.index_put_({ i, "..." }, torch::tensor(Xr[i], torch::kInt32));
}
states[className] = std::vector<int>(*max_element(y.begin(), y.end()) + 1);
iota(begin(states.at(className)), end(states.at(className)), 0);
return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
auto Xt = matrix2tensor(X);
auto yt = torch::tensor(y, torch::kInt32);
return { Xt, yt, features, handler.getClassName() };
}
// tuple<torch::Tensor, torch::Tensor, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::string& name, bool class_last)
// {
// auto [X, y, features, className] = loadArff(name, class_last);
// // Discretize the dataset
// torch::Tensor Xd;
// auto states = map<std::string, std::vector<int>>();
// // Fill the class states
// states[className] = std::vector<int>(*max_element(y.begin(), y.end()) + 1);
// iota(begin(states.at(className)), end(states.at(className)), 0);
// auto Xr = discretizeDataset(X, y);
// Xd = torch::zeros({ static_cast<int>(Xr.size()), static_cast<int>(Xr[0].size()) }, torch::kInt32);
// for (int i = 0; i < features.size(); ++i) {
// states[features[i]] = std::vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
// auto item = states.at(features[i]);
// iota(begin(item), end(item), 0);
// Xd.index_put_({ i, "..." }, torch::tensor(Xr[i], torch::kInt32));
// }
// auto yt = torch::tensor(y, torch::kInt32);
// return { Xd, yt, features, className, states };
// }
int main(int argc, char* argv[])
{
@@ -53,29 +74,42 @@ int main(int argc, char* argv[])
return 1;
}
std::string file_name = argv[1];
torch::Tensor X, y;
std::vector<std::string> features;
std::string className;
map<std::string, std::vector<int>> states;
auto clf = bayesnet::XBAODE(); // false for not using voting in predict
std::cout << "Library version: " << clf.getVersion() << std::endl;
tie(X, y, features, className, states) = loadDataset(file_name, true);
torch::Tensor weights = torch::full({ X.size(1) }, 15, torch::kDouble);
torch::Tensor dataset;
try {
auto yresized = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
dataset = torch::cat({ X, yresized }, 0);
std::string model_name = argv[2];
std::map<std::string, bayesnet::Classifier*> models{ {"TANLd", new bayesnet::TANLd()}, {"KDBLd", new bayesnet::KDBLd(2)}, {"AODELd", new bayesnet::AODELd() }
};
if (models.find(model_name) == models.end()) {
std::cerr << "Model not found: " << model_name << std::endl;
return 1;
}
catch (const std::exception& e) {
std::stringstream oss;
oss << "* Error in X and y dimensions *\n";
oss << "X dimensions: " << dataset.sizes() << "\n";
oss << "y dimensions: " << y.sizes();
throw std::runtime_error(oss.str());
auto clf = models[model_name];
std::cout << "Library version: " << clf->getVersion() << std::endl;
// auto [X, y, features, className, states] = loadDataset(file_name, true);
auto [Xt, yt, features, className] = loadArff(file_name, true);
std::map<std::string, std::vector<int>> states;
// int m = Xt.size(1);
// auto weights = torch::full({ m }, 1 / m, torch::kDouble);
// auto dataset = buildDataset(Xv, yv);
// try {
// auto yresized = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
// dataset = torch::cat({ X, yresized }, 0);
// }
// catch (const std::exception& e) {
// std::stringstream oss;
// oss << "* Error in X and y dimensions *\n";
// oss << "X dimensions: " << dataset.sizes() << "\n";
// oss << "y dimensions: " << y.sizes();
// throw std::runtime_error(oss.str());
// }
clf->fit(Xt, yt, features, className, states, bayesnet::Smoothing_t::ORIGINAL);
auto total = yt.size(0);
auto y_proba = clf->predict_proba(Xt);
auto y_pred = y_proba.argmax(1);
auto accuracy_value = (y_pred == yt).sum().item<float>() / total;
auto score = clf->score(Xt, yt);
std::cout << "File: " << file_name << " Model: " << model_name << " score: " << score << " Computed accuracy: " << accuracy_value << std::endl;
for (const auto clf : models) {
delete clf.second;
}
clf.fit(dataset, features, className, states, weights, bayesnet::Smoothing_t::LAPLACE);
auto score = clf.score(X, y);
std::cout << "File: " << file_name << " Model: BoostAODE score: " << score << std::endl;
return 0;
}

2
sample/vcpkg.json
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@@ -6,7 +6,7 @@
"fimdlp",
"libtorch-bin",
"folding",
"bayesnet"
"nlohmann-json"
],
"overrides": [
{