Fix conan (#10)

* Fix debug conan build target

* Add viewcoverage and fix coverage generation

* Add more tests to cover new integrity checks

* Add tests to accomplish 100%

* Fix conan-create makefile target

tests/Discretizer_unittest.cpp

@@ -13,6 +13,15 @@
 #include "BinDisc.h"
 #include "CPPFImdlp.h"
 
+#define EXPECT_THROW_WITH_MESSAGE(stmt, etype, whatstring) EXPECT_THROW( \
+try { \
+stmt; \
+} catch (const etype& ex) { \
+EXPECT_EQ(whatstring, std::string(ex.what())); \
+throw; \
+} \
+, etype)
+
 namespace mdlp {
     const float margin = 1e-4;
     static std::string set_data_path()
@@ -270,4 +279,110 @@ namespace mdlp {
             EXPECT_EQ(computed[i], expected[i]);
         }
     }
+
+    TEST(Discretizer, TransformEmptyData)
+    {
+        Discretizer* disc = new BinDisc(4, strategy_t::UNIFORM);
+        samples_t empty_data = {};
+        EXPECT_THROW_WITH_MESSAGE(disc->transform(empty_data), std::invalid_argument, "Data for transformation cannot be empty");
+        delete disc;
+    }
+
+    TEST(Discretizer, TransformNotFitted)
+    {
+        Discretizer* disc = new BinDisc(4, strategy_t::UNIFORM);
+        samples_t data = { 1.0f, 2.0f, 3.0f };
+        EXPECT_THROW_WITH_MESSAGE(disc->transform(data), std::runtime_error, "Discretizer not fitted yet or no valid cut points found");
+        delete disc;
+    }
+
+    TEST(Discretizer, TensorValidationFit)
+    {
+        Discretizer* disc = new BinDisc(4, strategy_t::UNIFORM);
+
+        auto X = torch::tensor({ 1.0f, 2.0f, 3.0f }, torch::kFloat32);
+        auto y = torch::tensor({ 1, 2, 3 }, torch::kInt32);
+
+        // Test non-1D tensors
+        auto X_2d = torch::tensor({ {1.0f, 2.0f}, {3.0f, 4.0f} }, torch::kFloat32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_t(X_2d, y), std::invalid_argument, "Only 1D tensors supported");
+
+        auto y_2d = torch::tensor({ {1, 2}, {3, 4} }, torch::kInt32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_t(X, y_2d), std::invalid_argument, "Only 1D tensors supported");
+
+        // Test wrong tensor types
+        auto X_int = torch::tensor({ 1, 2, 3 }, torch::kInt32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_t(X_int, y), std::invalid_argument, "X tensor must be Float32 type");
+
+        auto y_float = torch::tensor({ 1.0f, 2.0f, 3.0f }, torch::kFloat32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_t(X, y_float), std::invalid_argument, "y tensor must be Int32 type");
+
+        // Test mismatched sizes
+        auto y_short = torch::tensor({ 1, 2 }, torch::kInt32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_t(X, y_short), std::invalid_argument, "X and y tensors must have same number of elements");
+
+        // Test empty tensors
+        auto X_empty = torch::tensor({}, torch::kFloat32);
+        auto y_empty = torch::tensor({}, torch::kInt32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_t(X_empty, y_empty), std::invalid_argument, "Tensors cannot be empty");
+
+        delete disc;
+    }
+
+    TEST(Discretizer, TensorValidationTransform)
+    {
+        Discretizer* disc = new BinDisc(4, strategy_t::UNIFORM);
+
+        // First fit with valid data
+        auto X_fit = torch::tensor({ 1.0f, 2.0f, 3.0f, 4.0f }, torch::kFloat32);
+        auto y_fit = torch::tensor({ 1, 2, 3, 4 }, torch::kInt32);
+        disc->fit_t(X_fit, y_fit);
+
+        // Test non-1D tensor
+        auto X_2d = torch::tensor({ {1.0f, 2.0f}, {3.0f, 4.0f} }, torch::kFloat32);
+        EXPECT_THROW_WITH_MESSAGE(disc->transform_t(X_2d), std::invalid_argument, "Only 1D tensors supported");
+
+        // Test wrong tensor type
+        auto X_int = torch::tensor({ 1, 2, 3 }, torch::kInt32);
+        EXPECT_THROW_WITH_MESSAGE(disc->transform_t(X_int), std::invalid_argument, "X tensor must be Float32 type");
+
+        // Test empty tensor
+        auto X_empty = torch::tensor({}, torch::kFloat32);
+        EXPECT_THROW_WITH_MESSAGE(disc->transform_t(X_empty), std::invalid_argument, "Tensor cannot be empty");
+
+        delete disc;
+    }
+
+    TEST(Discretizer, TensorValidationFitTransform)
+    {
+        Discretizer* disc = new BinDisc(4, strategy_t::UNIFORM);
+
+        auto X = torch::tensor({ 1.0f, 2.0f, 3.0f }, torch::kFloat32);
+        auto y = torch::tensor({ 1, 2, 3 }, torch::kInt32);
+
+        // Test non-1D tensors
+        auto X_2d = torch::tensor({ {1.0f, 2.0f}, {3.0f, 4.0f} }, torch::kFloat32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_transform_t(X_2d, y), std::invalid_argument, "Only 1D tensors supported");
+
+        auto y_2d = torch::tensor({ {1, 2}, {3, 4} }, torch::kInt32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_transform_t(X, y_2d), std::invalid_argument, "Only 1D tensors supported");
+
+        // Test wrong tensor types
+        auto X_int = torch::tensor({ 1, 2, 3 }, torch::kInt32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_transform_t(X_int, y), std::invalid_argument, "X tensor must be Float32 type");
+
+        auto y_float = torch::tensor({ 1.0f, 2.0f, 3.0f }, torch::kFloat32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_transform_t(X, y_float), std::invalid_argument, "y tensor must be Int32 type");
+
+        // Test mismatched sizes
+        auto y_short = torch::tensor({ 1, 2 }, torch::kInt32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_transform_t(X, y_short), std::invalid_argument, "X and y tensors must have same number of elements");
+
+        // Test empty tensors
+        auto X_empty = torch::tensor({}, torch::kFloat32);
+        auto y_empty = torch::tensor({}, torch::kInt32);
+        EXPECT_THROW_WITH_MESSAGE(disc->fit_transform_t(X_empty, y_empty), std::invalid_argument, "Tensors cannot be empty");
+
+        delete disc;
+    }
 }