[Feature] add length of data dividing term to gradient, hessian (#47)

Related to #44

mqboost/objective.py

@@ -7,8 +7,8 @@
 from mqboost.utils import delta_validate, epsilon_validate
 
 CHECK_LOSS: str = "check_loss"
-GradFnLike = Callable[[Any], np.ndarray]
-HessFnLike = Callable[[Any], np.ndarray]
+GradFnLike = Callable[[np.ndarray, float, Any], np.ndarray]
+HessFnLike = Callable[[np.ndarray, float, Any], np.ndarray]
 ObjLike = Callable[
     [np.ndarray, DtrainLike, list[float], Any], tuple[np.ndarray, np.ndarray]
 ]
@@ -79,14 +79,15 @@ def _compute_grads_hess(
         _y_train, _y_pred = _train_pred_reshape(
             y_pred=y_pred, dtrain=dtrain, len_alpha=_len_alpha
         )
-        grads = []
-        hess = []
+        grads: list[np.ndarray] = []
+        hess: list[np.ndarray] = []
+        _len_y = len(_y_train[0])
         for alpha_inx in range(len(alphas)):
-            _err_for_alpha = _y_train[alpha_inx] - _y_pred[alpha_inx]
+            _err_for_alpha: np.ndarray = _y_train[alpha_inx] - _y_pred[alpha_inx]
             _grad = grad_fn(error=_err_for_alpha, alpha=alphas[alpha_inx], **kwargs)
             _hess = hess_fn(error=_err_for_alpha, alpha=alphas[alpha_inx], **kwargs)
-            grads.append(_grad)
-            hess.append(_hess)
+            grads.append(_grad / _len_y)
+            hess.append(_hess / _len_y)
 
         return np.concatenate(grads), np.concatenate(hess)
 

tests/test_objective.py

@@ -81,7 +81,7 @@ def test_check_loss_grad_hess(dummy_data):
     dtrain = dummy_data(y_true)
     grads, hess = check_loss_grad_hess(y_pred=y_pred, dtrain=dtrain, alphas=alphas)
     # fmt: off
-    expected_grads = [-0.1, -0.1, 0.9, -0.1, -0.1, -0.5, -0.5, 0.5, -0.5, -0.5, -0.9, -0.9, 0.1, -0.9, -0.9]
+    expected_grads = [-0.02, -0.02, 0.18, -0.02, -0.02, -0.1, -0.1, 0.1, -0.1, -0.1, -0.18, -0.18, 0.02, -0.18, -0.18]
     # fmt: on
     np.testing.assert_almost_equal(grads, np.array(expected_grads))
     assert grads.shape == hess.shape
@@ -92,9 +92,9 @@ def test_check_loss_grad_hess(dummy_data):
 @pytest.mark.parametrize(
     "delta, expected_grads",
     [
-        (0.01, [-0.1, -0.1, 0.9, -0.1, -0.1, -0.5, -0.5, 0.5, -0.5, -0.5, -0.9, -0.9, 0.1, -0.9, -0.9]),
-        (0.02, [0.001, 0.001, 0.9, -0.1, -0.1, 0.005, 0.005, 0.5, -0.5, -0.5, 0.009, 0.009, 0.1, -0.9, -0.9]),
-        (0.05, [0.001, 0.001, 0.018, 0.003, 0.005, 0.005, 0.005, 0.01, 0.015, 0.025, 0.009, 0.009, 0.002, 0.027, 0.045]),
+        (0.01, [-0.02, -0.02, 0.18, -0.02, -0.02, -0.1, -0.1, 0.1, -0.1, -0.1, -0.18, -0.18, 0.02, -0.18, -0.18]),
+        (0.02, [0.0002, 0.0002, 0.18, -0.02, -0.02, 0.001, 0.001, 0.1, -0.1, -0.1, 0.0018, 0.0018, 0.02, -0.18, -0.18]),
+        (0.05, [0.0002, 0.0002, 0.0036, 0.0006, 0.001, 0.001, 0.001, 0.002, 0.003, 0.005, 0.0018, 0.0018, 0.0004, 0.0054, 0.009]),
     ],
 )
 # fmt: on
@@ -116,18 +116,18 @@ def test_huber_loss_grad_hess(dummy_data, delta, expected_grads):
     [
         (
             0.01,
-            [0.15, 0.15, 0.7333, 0.025, -0.0167, -0.25, -0.25, 0.3333, -0.375, -0.4167, -0.65, -0.65, -0.0667, -0.775, -0.8167],
-            [25.0, 25.0, 16.6666, 12.5, 8.3333, 25.0, 25.0, 16.6666, 12.5, 8.33333333, 25.0, 25.0, 16.6666, 12.5, 8.33333333],
+            [0.03, 0.03, 0.1467, 0.005, -0.0033, -0.05, -0.05, 0.0667, -0.075, -0.0833, -0.13, -0.13, -0.0133, -0.155, -0.1633],
+            [5.0, 5.0, 3.3333, 2.5, 1.6667, 5.0, 5.0, 3.3333, 2.5, 1.66666667, 5.0, 5.0, 3.3333, 2.5, 1.66666667],
         ),
         (
             0.005,
-            [0.0667, 0.0667, 0.8, -0.02857, -0.0545, -0.3333, -0.3333, 0.4, -0.4286, -0.4545, -0.7333, -0.73333, 0.0, -0.8286, -0.8545],
-            [33.3333, 33.3333, 20.0, 14.2857, 9.0909, 33.3333, 33.3333, 20.0, 14.2857, 9.0909, 33.3333, 33.3333, 20.0, 14.2857, 9.0909],
+            [0.0133, 0.0133, 0.16, -0.00571, -0.0109, -0.0667, -0.0667, 0.08, -0.0857, -0.0909, -0.1467, -0.14667, 0.0, -0.1657, -0.1709],
+            [6.6667, 6.6667, 4.0, 2.8571, 1.8182, 6.6667, 6.6667, 4.0, 2.8571, 1.8182, 6.6667, 6.6667, 4.0, 2.8571, 1.8182],
         ),
         (
             0.001,
-            [-0.0545, -0.0545, 0.8762, -0.0838, -0.0902, -0.4545, -0.4545, 0.4762, -0.4839, -0.4902, -0.8545, -0.8545, 0.0762, -0.8839, -0.8902],
-            [45.4545, 45.4545, 23.8095, 16.1290, 9.8039, 45.4545, 45.4545, 23.8095, 16.1290, 9.8039, 45.4545, 45.4545, 23.8095, 16.1290, 9.8039],
+            [-0.0109, -0.0109, 0.1752, -0.01676, -0.01804, -0.0909, -0.0909, 0.0952, -0.09678, -0.09804, -0.1709, -0.1709, 0.01524, -0.17678, -0.17804],
+            [9.0909, 9.0909, 4.7619, 3.2258, 1.9608, 9.0909, 9.0909, 4.7619, 3.2258, 1.9608, 9.0909, 9.0909, 4.7619, 3.2258, 1.9608],
         ),
     ],
 )