Fix tests

include/LightGBM/objective_function.h

@@ -70,7 +70,7 @@ class ObjectiveFunction {
   virtual void ConvertOutputs(const int num_data, const double* inputs, double* outputs) const {
     #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)
     for (int i = 0; i < num_data; i ++) {
-      ConvertOutput(inputs + i, outputs + i);
+      ConvertOutput(&inputs[i], &outputs[i]);
     }
   }
 

python-package/lightgbm/basic.py

@@ -5386,7 +5386,14 @@ def get_gradients(self, y_pred: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
         if self.num_data is None or self.num_class is None:
             raise ValueError("ObjectiveFunction was not created properly")
 
+        if y_pred.shape[0] != self.num_data:
+            raise ValueError("Gradients cannot be computed as the number of predictions is wrong")
+
+        if self.num_class != 1 and (y_pred.ndim != 2 or y_pred.shape[1] != self.num_class):
+            raise ValueError("Multiclass gradient computation should be called with the correct shape")
+
         data_shape = self.num_data * self.num_class
+        y_pred = np.asfortranarray(y_pred)
         grad = np.empty(dtype=np.float32, shape=data_shape)
         hess = np.empty(dtype=np.float32, shape=data_shape)
 

src/objective/multiclass_objective.hpp

@@ -132,7 +132,7 @@ class MulticlassSoftmax: public ObjectiveFunction {
   void ConvertOutputs(const int num_data, const double* inputs, double* outputs) const override {
     #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)
     for (int i = 0; i < num_data; i += num_class_) {
-      ConvertOutput(inputs + i, outputs + i);
+      ConvertOutput(&inputs[i], &outputs[i]);
     }
   }
 
@@ -246,7 +246,7 @@ class MulticlassOVA: public ObjectiveFunction {
   void ConvertOutputs(const int num_data, const double* inputs, double* outputs) const override {
     #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)
     for (int i = 0; i < num_data; i += num_class_) {
-      ConvertOutput(inputs + i, outputs + i);
+      ConvertOutput(&inputs[i], &outputs[i]);
     }
   }
 

tests/python_package_test/test_engine.py

@@ -4450,7 +4450,7 @@ def loss(y_pred, dtrain):
     np.testing.assert_allclose(booster_exposed.predict(X), booster.predict(X, raw_score=True))
     np.testing.assert_allclose(booster_exposed.predict(X), booster_custom.predict(X))
 
-    y_pred = np.zeros_like(booster.predict(X, raw_score=True))
+    y_pred = booster.predict(X, raw_score=True)
     np.testing.assert_allclose(builtin_loss(y_pred, lgb_train), custom_objective(y_pred, lgb_train))
 
     np.testing.assert_allclose(builtin_convert_scores(booster_exposed.predict(X)), booster.predict(X))