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Add AUC computing in Experiment and store in result

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This commit is contained in:
Ricardo Montañana Gómez
2024-07-12 17:23:03 +02:00
parent 26dfe6d056
commit 84adf13a79
7 changed files with 134 additions and 7 deletions
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23
src/main/Experiment.cpp
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@@ -3,6 +3,7 @@
#include "common/Paths.h"
#include "Models.h"
#include "Scores.h"
#include "RocAuc.h"
#include "Experiment.h"
namespace platform {
using json = nlohmann::ordered_json;
@@ -160,6 +161,8 @@ namespace platform {
int nResults = nfolds * static_cast<int>(randomSeeds.size());
auto accuracy_test = torch::zeros({ nResults }, torch::kFloat64);
auto accuracy_train = torch::zeros({ nResults }, torch::kFloat64);
auto auc_test = torch::zeros({ nResults }, torch::kFloat64);
auto auc_train = torch::zeros({ nResults }, torch::kFloat64);
auto train_time = torch::zeros({ nResults }, torch::kFloat64);
auto test_time = torch::zeros({ nResults }, torch::kFloat64);
auto nodes = torch::zeros({ nResults }, torch::kFloat64);
@@ -228,10 +231,13 @@ namespace platform {
//
// Score train
//
double auc_train_value = 0;
if (!no_train_score) {
auto y_predict = clf->predict(X_train);
Scores scores(y_train, y_predict, num_classes, labels);
auto roc_auc = RocAuc();
auto y_proba_train = clf->predict_proba(X_train);
Scores scores(y_train, y_proba_train, num_classes, labels);
accuracy_train_value = scores.accuracy();
auc_train_value = roc_auc.compute(y_proba_train, y_train);
confusion_matrices_train.push_back(scores.get_confusion_matrix_json(true));
}
//
@@ -240,10 +246,15 @@ namespace platform {
if (!quiet)
showProgress(nfold + 1, getColor(clf->getStatus()), "c");
test_timer.start();
auto y_predict = clf->predict(X_test);
Scores scores(y_test, y_predict, num_classes, labels);
// auto y_predict = clf->predict(X_test);
auto y_proba_test = clf->predict_proba(X_test);
Scores scores(y_test, y_proba_test, num_classes, labels);
auto accuracy_test_value = scores.accuracy();
auto roc_auc = RocAuc();
double auc_test_value = roc_auc.compute(y_proba_test, y_test);
test_time[item] = test_timer.getDuration();
auc_train[item] = auc_train_value;
auc_test[item] = auc_test_value;
accuracy_train[item] = accuracy_train_value;
accuracy_test[item] = accuracy_test_value;
confusion_matrices.push_back(scores.get_confusion_matrix_json(true));
@@ -252,6 +263,8 @@ namespace platform {
//
// Store results and times in std::vector
//
partial_result.addAucTrain(auc_train_value);
partial_result.addAucTest(auc_test_value);
partial_result.addScoreTrain(accuracy_train_value);
partial_result.addScoreTest(accuracy_test_value);
partial_result.addTimeTrain(train_time[item].item<double>());
@@ -275,6 +288,8 @@ namespace platform {
partial_result.setGraph(graphs);
partial_result.setScoreTest(torch::mean(accuracy_test).item<double>()).setScoreTrain(torch::mean(accuracy_train).item<double>());
partial_result.setScoreTestStd(torch::std(accuracy_test).item<double>()).setScoreTrainStd(torch::std(accuracy_train).item<double>());
partial_result.setAucTest(torch::mean(auc_test).item<double>()).setAucTrain(torch::mean(auc_train).item<double>());
partial_result.setAucTestStd(torch::std(auc_test).item<double>()).setAucTrainStd(torch::std(auc_train).item<double>());
partial_result.setTrainTime(torch::mean(train_time).item<double>()).setTestTime(torch::mean(test_time).item<double>());
partial_result.setTestTimeStd(torch::std(test_time).item<double>()).setTrainTimeStd(torch::std(train_time).item<double>());
partial_result.setNodes(torch::mean(nodes).item<double>()).setLeaves(torch::mean(edges).item<double>()).setDepth(torch::mean(num_states).item<double>());