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Begin model inclusion

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This commit is contained in:
Ricardo Montañana Gómez
2025-02-18 10:48:46 +01:00
parent 17728212c1
commit bd5ba14f04
14 changed files with 967 additions and 71 deletions
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166
sample/sample.cpp
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@@ -9,6 +9,7 @@
#include <fimdlp/CPPFImdlp.h>
#include <folding.hpp>
#include <bayesnet/utils/BayesMetrics.h>
#include <bayesnet/classifiers/SPODE.h>
#include "Models.h"
#include "modelRegister.h"
#include "config_platform.h"
@@ -160,82 +161,119 @@ int main(int argc, char** argv)
states[feature] = std::vector<int>(maxes[feature]);
}
states[className] = std::vector<int>(maxes[className]);
auto clf = platform::Models::instance()->create(model_name);
// Output the states
std::cout << std::string(80, '-') << std::endl;
std::cout << "States" << std::endl;
for (auto feature : features) {
std::cout << feature << ": " << states[feature].size() << std::endl;
}
std::cout << std::string(80, '-') << std::endl;
//auto clf = platform::Models::instance()->create("SPODE");
auto clf = bayesnet::SPODE(2);
bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::ORIGINAL;
clf->fit(Xd, y, features, className, states, smoothing);
clf.fit(Xd, y, features, className, states, smoothing);
if (dump_cpt) {
std::cout << "--- CPT Tables ---" << std::endl;
clf->dump_cpt();
std::cout << clf.dump_cpt();
}
auto lines = clf->show();
std::cout << "--- Datos predicción ---" << std::endl;
std::cout << "Orden de variables: " << std::endl;
for (auto feature : features) {
std::cout << feature << ", ";
}
std::cout << std::endl;
std::cout << "X[0]: ";
for (int i = 0; i < Xd.size(); ++i) {
std::cout << Xd[i][0] << ", ";
}
std::cout << std::endl;
std::cout << std::string(80, '-') << std::endl;
auto lines = clf.show();
for (auto line : lines) {
std::cout << line << std::endl;
}
std::cout << "--- Topological Order ---" << std::endl;
auto order = clf->topological_order();
auto order = clf.topological_order();
for (auto name : order) {
std::cout << name << ", ";
}
std::cout << "end." << std::endl;
auto score = clf->score(Xd, y);
std::cout << "Score: " << score << std::endl;
auto graph = clf->graph();
auto dot_file = model_name + "_" + file_name;
ofstream file(dot_file + ".dot");
file << graph;
file.close();
std::cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << std::endl;
std::cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << std::endl;
std::string stratified_string = stratified ? " Stratified" : "";
std::cout << nFolds << " Folds" << stratified_string << " Cross validation" << std::endl;
std::cout << "==========================================" << std::endl;
torch::Tensor Xt = torch::zeros({ static_cast<int>(Xd.size()), static_cast<int>(Xd[0].size()) }, torch::kInt32);
torch::Tensor yt = torch::tensor(y, torch::kInt32);
for (int i = 0; i < features.size(); ++i) {
Xt.index_put_({ i, "..." }, torch::tensor(Xd[i], torch::kInt32));
}
float total_score = 0, total_score_train = 0, score_train, score_test;
folding::Fold* fold;
double nodes = 0.0;
if (stratified)
fold = new folding::StratifiedKFold(nFolds, y, seed);
else
fold = new folding::KFold(nFolds, y.size(), seed);
for (auto i = 0; i < nFolds; ++i) {
auto [train, test] = fold->getFold(i);
std::cout << "Fold: " << i + 1 << std::endl;
if (tensors) {
auto ttrain = torch::tensor(train, torch::kInt64);
auto ttest = torch::tensor(test, torch::kInt64);
torch::Tensor Xtraint = torch::index_select(Xt, 1, ttrain);
torch::Tensor ytraint = yt.index({ ttrain });
torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
torch::Tensor ytestt = yt.index({ ttest });
clf->fit(Xtraint, ytraint, features, className, states, smoothing);
auto temp = clf->predict(Xtraint);
score_train = clf->score(Xtraint, ytraint);
score_test = clf->score(Xtestt, ytestt);
} else {
auto [Xtrain, ytrain] = extract_indices(train, Xd, y);
auto [Xtest, ytest] = extract_indices(test, Xd, y);
clf->fit(Xtrain, ytrain, features, className, states, smoothing);
std::cout << "Nodes: " << clf->getNumberOfNodes() << std::endl;
nodes += clf->getNumberOfNodes();
score_train = clf->score(Xtrain, ytrain);
score_test = clf->score(Xtest, ytest);
auto predict_proba = clf.predict_proba(Xd);
std::cout << "Instances predict_proba: ";
for (int i = 0; i < predict_proba.size(); i++) {
std::cout << "Instance " << i << ": ";
for (int j = 0; j < 4; ++j) {
std::cout << Xd[j][i] << ", ";
}
if (dump_cpt) {
std::cout << "--- CPT Tables ---" << std::endl;
std::cout << clf->dump_cpt();
std::cout << ": ";
for (auto score : predict_proba[i]) {
std::cout << score << ", ";
}
total_score_train += score_train;
total_score += score_test;
std::cout << "Score Train: " << score_train << std::endl;
std::cout << "Score Test : " << score_test << std::endl;
std::cout << "-------------------------------------------------------------------------------" << std::endl;
std::cout << std::endl;
}
std::cout << "Nodes: " << nodes / nFolds << std::endl;
std::cout << "**********************************************************************************" << std::endl;
std::cout << "Average Score Train: " << total_score_train / nFolds << std::endl;
std::cout << "Average Score Test : " << total_score / nFolds << std::endl;return 0;
// std::cout << std::endl;
// std::cout << "end." << std::endl;
// auto score = clf->score(Xd, y);
// std::cout << "Score: " << score << std::endl;
// auto graph = clf->graph();
// auto dot_file = model_name + "_" + file_name;
// ofstream file(dot_file + ".dot");
// file << graph;
// file.close();
// std::cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << std::endl;
// std::cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << std::endl;
// std::string stratified_string = stratified ? " Stratified" : "";
// std::cout << nFolds << " Folds" << stratified_string << " Cross validation" << std::endl;
// std::cout << "==========================================" << std::endl;
// torch::Tensor Xt = torch::zeros({ static_cast<int>(Xd.size()), static_cast<int>(Xd[0].size()) }, torch::kInt32);
// torch::Tensor yt = torch::tensor(y, torch::kInt32);
// for (int i = 0; i < features.size(); ++i) {
// Xt.index_put_({ i, "..." }, torch::tensor(Xd[i], torch::kInt32));
// }
// float total_score = 0, total_score_train = 0, score_train, score_test;
// folding::Fold* fold;
// double nodes = 0.0;
// if (stratified)
// fold = new folding::StratifiedKFold(nFolds, y, seed);
// else
// fold = new folding::KFold(nFolds, y.size(), seed);
// for (auto i = 0; i < nFolds; ++i) {
// auto [train, test] = fold->getFold(i);
// std::cout << "Fold: " << i + 1 << std::endl;
// if (tensors) {
// auto ttrain = torch::tensor(train, torch::kInt64);
// auto ttest = torch::tensor(test, torch::kInt64);
// torch::Tensor Xtraint = torch::index_select(Xt, 1, ttrain);
// torch::Tensor ytraint = yt.index({ ttrain });
// torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
// torch::Tensor ytestt = yt.index({ ttest });
// clf->fit(Xtraint, ytraint, features, className, states, smoothing);
// auto temp = clf->predict(Xtraint);
// score_train = clf->score(Xtraint, ytraint);
// score_test = clf->score(Xtestt, ytestt);
// } else {
// auto [Xtrain, ytrain] = extract_indices(train, Xd, y);
// auto [Xtest, ytest] = extract_indices(test, Xd, y);
// clf->fit(Xtrain, ytrain, features, className, states, smoothing);
// std::cout << "Nodes: " << clf->getNumberOfNodes() << std::endl;
// nodes += clf->getNumberOfNodes();
// score_train = clf->score(Xtrain, ytrain);
// score_test = clf->score(Xtest, ytest);
// }
// // if (dump_cpt) {
// // std::cout << "--- CPT Tables ---" << std::endl;
// // std::cout << clf->dump_cpt();
// // }
// total_score_train += score_train;
// total_score += score_test;
// std::cout << "Score Train: " << score_train << std::endl;
// std::cout << "Score Test : " << score_test << std::endl;
// std::cout << "-------------------------------------------------------------------------------" << std::endl;
// }
// std::cout << "Nodes: " << nodes / nFolds << std::endl;
// std::cout << "**********************************************************************************" << std::endl;
// std::cout << "Average Score Train: " << total_score_train / nFolds << std::endl;
// std::cout << "Average Score Test : " << total_score / nFolds << std::endl;return 0;
}