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Add predict_proba with tensors

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Ricardo Montañana Gómez
2024-07-12 12:54:30 +02:00
parent c5ff1a0b2b
commit 37716a57f4
6 changed files with 66 additions and 9 deletions
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31
tests/TestPythonClassifiers.cc
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@@ -13,8 +13,6 @@
#include "pyclfs/ODTE.h"
#include "TestUtils.h"
const std::string ACTUAL_VERSION = "1.0.5";
TEST_CASE("Test Python Classifiers score", "[PyClassifiers]")
{
map <pair<std::string, std::string>, float> scores = {
@@ -37,15 +35,17 @@ TEST_CASE("Test Python Classifiers score", "[PyClassifiers]")
map<std::string, std::string> versions = {
{"ODTE", "0.3.6"},
{"STree", "1.3.2"},
{"SVC", "1.3.2"},
{"RandomForest", "1.3.2"}
{"SVC", "1.5.0"},
{"RandomForest", "1.5.0"}
};
auto clf = models[name];
SECTION("Test Python Classifier " + name + " score ")
{
auto random_state = nlohmann::json::parse("{ \"random_state\": 0 }");
for (std::string file_name : { "glass", "iris", "ecoli", "diabetes" }) {
auto raw = RawDatasets(file_name, false);
clf->setHyperparameters(random_state);
clf->fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
auto score = clf->score(raw.Xt, raw.yt);
INFO("File: " + file_name + " Classifier: " + name + " Score: " + to_string(score));
@@ -82,6 +82,29 @@ TEST_CASE("Classifier with discretized dataset", "[PyClassifiers]")
auto score = clf.score(raw.Xt, raw.yt);
REQUIRE(score == Catch::Approx(0.96667f).epsilon(raw.epsilon));
}
TEST_CASE("Predict with non_discretized dataset and comparing to predict_proba", "[PyClassifiers]")
{
auto raw = RawDatasets("iris", false);
auto clf = pywrap::STree();
clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
auto predictions = clf.predict(raw.Xt);
auto probabilities = clf.predict_proba(raw.Xt);
auto preds = probabilities.argmax(1);
auto classNumStates = torch::max(raw.yt).item<int>() + 1;
REQUIRE(predictions.size(0) == probabilities.size(0));
REQUIRE(predictions.size(0) == preds.size(0));
REQUIRE(probabilities.size(1) == classNumStates);
int right = 0;
for (std::size_t i = 0; i < predictions.size(0); ++i) {
if (predictions[i].item<int>() == preds[i].item<int>()) {
right++;
}
REQUIRE(predictions[i].item<int>() == preds[i].item<int>());
}
auto accuracy = right / static_cast<float>(predictions.size(0));
REQUIRE(accuracy == Catch::Approx(1.0f).epsilon(raw.epsilon));
}
// TEST_CASE("XGBoost", "[PyClassifiers]")
// {
// auto raw = RawDatasets("iris", true);