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Add Scores class and TestsScores

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Ricardo Montañana Gómez
2024-05-10 00:51:21 +02:00
parent 6d1b78ada7
commit 009ed037b8
6 changed files with 316 additions and 7 deletions
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tests/TestScores.cpp Normal file
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#include <catch2/catch_test_macros.hpp>
#include <catch2/catch_approx.hpp>
#include <vector>
#include <string>
#include "TestUtils.h"
#include "results/Result.h"
#include "common/DotEnv.h"
#include "common/Datasets.h"
#include "common/Paths.h"
#include "main/Scores.h"
#include "config.h"
auto epsilon = 1e-4;
void make_test_bin(int TP, int TN, int FP, int FN, std::vector<int>& y_test, std::vector<int>& y_pred)
{
// TP
for (int i = 0; i < TP; i++) {
y_test.push_back(1);
y_pred.push_back(1);
}
// TN
for (int i = 0; i < TN; i++) {
y_test.push_back(0);
y_pred.push_back(0);
}
// FP
for (int i = 0; i < FP; i++) {
y_test.push_back(0);
y_pred.push_back(1);
}
// FN
for (int i = 0; i < FN; i++) {
y_test.push_back(1);
y_pred.push_back(0);
}
}
TEST_CASE("TestScores binary", "[Scores]")
{
std::vector<int> y_test;
std::vector<int> y_pred;
make_test_bin(197, 210, 52, 41, y_test, y_pred);
auto y_test_tensor = torch::tensor(y_test, torch::kInt32);
auto y_pred_tensor = torch::tensor(y_pred, torch::kInt32);
platform::Scores scores(y_test_tensor, y_pred_tensor, 2);
REQUIRE(scores.accuracy() == Catch::Approx(0.814).epsilon(epsilon));
REQUIRE(scores.f1_score(0) == Catch::Approx(0.818713));
REQUIRE(scores.f1_score(1) == Catch::Approx(0.809035));
REQUIRE(scores.precision(0) == Catch::Approx(0.836653));
REQUIRE(scores.precision(1) == Catch::Approx(0.791165));
REQUIRE(scores.recall(0) == Catch::Approx(0.801527));
REQUIRE(scores.recall(1) == Catch::Approx(0.827731));
REQUIRE(scores.f1_weighted() == Catch::Approx(0.814106));
REQUIRE(scores.f1_macro() == Catch::Approx(0.813874));
auto confusion_matrix = scores.get_confusion_matrix();
REQUIRE(confusion_matrix[0][0].item<int>() == 210);
REQUIRE(confusion_matrix[0][1].item<int>() == 52);
REQUIRE(confusion_matrix[1][0].item<int>() == 41);
REQUIRE(confusion_matrix[1][1].item<int>() == 197);
}
TEST_CASE("TestScores multiclass", "[Scores]")
{
std::vector<int> y_test = { 0, 2, 2, 2, 2, 0, 1, 2, 0, 2 };
std::vector<int> y_pred = { 0, 1, 2, 2, 1, 1, 1, 0, 0, 2 };
auto y_test_tensor = torch::tensor(y_test, torch::kInt32);
auto y_pred_tensor = torch::tensor(y_pred, torch::kInt32);
platform::Scores scores(y_test_tensor, y_pred_tensor, 3);
REQUIRE(scores.accuracy() == Catch::Approx(0.6).epsilon(epsilon));
REQUIRE(scores.f1_score(0) == Catch::Approx(0.666667));
REQUIRE(scores.f1_score(1) == Catch::Approx(0.4));
REQUIRE(scores.f1_score(2) == Catch::Approx(0.666667));
REQUIRE(scores.precision(0) == Catch::Approx(0.666667));
REQUIRE(scores.precision(1) == Catch::Approx(0.25));
REQUIRE(scores.precision(2) == Catch::Approx(1.0));
REQUIRE(scores.recall(0) == Catch::Approx(0.666667));
REQUIRE(scores.recall(1) == Catch::Approx(1.0));
REQUIRE(scores.recall(2) == Catch::Approx(0.5));
REQUIRE(scores.f1_weighted() == Catch::Approx(0.64));
REQUIRE(scores.f1_macro() == Catch::Approx(0.577778));
}
TEST_CASE("Test Confusion Matrix Values", "[Scores]")
{
std::vector<int> y_test = { 0, 2, 2, 2, 2, 0, 1, 2, 0, 2 };
std::vector<int> y_pred = { 0, 1, 2, 2, 1, 1, 1, 0, 0, 2 };
auto y_test_tensor = torch::tensor(y_test, torch::kInt32);
auto y_pred_tensor = torch::tensor(y_pred, torch::kInt32);
platform::Scores scores(y_test_tensor, y_pred_tensor, 3);
auto confusion_matrix = scores.get_confusion_matrix();
REQUIRE(confusion_matrix[0][0].item<int>() == 2);
REQUIRE(confusion_matrix[0][1].item<int>() == 1);
REQUIRE(confusion_matrix[0][2].item<int>() == 0);
REQUIRE(confusion_matrix[1][0].item<int>() == 0);
REQUIRE(confusion_matrix[1][1].item<int>() == 1);
REQUIRE(confusion_matrix[1][2].item<int>() == 0);
REQUIRE(confusion_matrix[2][0].item<int>() == 1);
REQUIRE(confusion_matrix[2][1].item<int>() == 2);
REQUIRE(confusion_matrix[2][2].item<int>() == 3);
}
TEST_CASE("Confusion Matrix JSON", "[Scores]")
{
std::vector<int> y_test = { 0, 2, 2, 2, 2, 0, 1, 2, 0, 2 };
std::vector<int> y_pred = { 0, 1, 2, 2, 1, 1, 1, 0, 0, 2 };
auto y_test_tensor = torch::tensor(y_test, torch::kInt32);
auto y_pred_tensor = torch::tensor(y_pred, torch::kInt32);
std::vector<std::string> labels = { "Aeroplane", "Boat", "Car" };
platform::Scores scores(y_test_tensor, y_pred_tensor, 3, labels);
auto res_json_int = scores.get_confusion_matrix_json();
REQUIRE(res_json_int[0][0] == 2);
REQUIRE(res_json_int[0][1] == 1);
REQUIRE(res_json_int[0][2] == 0);
REQUIRE(res_json_int[1][0] == 0);
REQUIRE(res_json_int[1][1] == 1);
REQUIRE(res_json_int[1][2] == 0);
REQUIRE(res_json_int[2][0] == 1);
REQUIRE(res_json_int[2][1] == 2);
REQUIRE(res_json_int[2][2] == 3);
auto res_json_str = scores.get_confusion_matrix_json(true);
REQUIRE(res_json_str["Aeroplane"][0] == 2);
REQUIRE(res_json_str["Aeroplane"][1] == 1);
REQUIRE(res_json_str["Aeroplane"][2] == 0);
REQUIRE(res_json_str["Boat"][0] == 0);
REQUIRE(res_json_str["Boat"][1] == 1);
REQUIRE(res_json_str["Boat"][2] == 0);
REQUIRE(res_json_str["Car"][0] == 1);
REQUIRE(res_json_str["Car"][1] == 2);
REQUIRE(res_json_str["Car"][2] == 3);
}
TEST_CASE("Classification Report", "[Scores]")
{
std::vector<int> y_test = { 0, 2, 2, 2, 2, 0, 1, 2, 0, 2 };
std::vector<int> y_pred = { 0, 1, 2, 2, 1, 1, 1, 0, 0, 2 };
auto y_test_tensor = torch::tensor(y_test, torch::kInt32);
auto y_pred_tensor = torch::tensor(y_pred, torch::kInt32);
std::vector<std::string> labels = { "Aeroplane", "Boat", "Car" };
platform::Scores scores(y_test_tensor, y_pred_tensor, 3, labels);
std::string expected = R"(Classification Report
=====================
precision recall f1-score support
========= ========= ========= =========
Aeroplane 0.6666667 0.6666667 0.6666667 3
Boat 0.2500000 1.0000000 0.4000000 1
Car 1.0000000 0.5000000 0.6666667 6
accuracy 0.6000000 10
macro avg 0.6388889 0.7222223 0.5777778 10
weighted avg 0.8250000 0.6000000 0.6400000 10
)";
REQUIRE(scores.classification_report() == expected);
}