Fix uniform sample weight knot placement

src/splinator/estimators.py

@@ -500,6 +500,8 @@ def fit(self, X, y, sample_weight=None):
         # Validate sample_weight
         if sample_weight is not None:
             sample_weight = np.asarray(sample_weight)
+            if sample_weight.ndim != 1:
+                raise ValueError("sample_weight must be a 1-D array")
             if sample_weight.shape[0] != X.shape[0]:
                 raise ValueError(
                     f"sample_weight has {sample_weight.shape[0]} samples, "

src/splinator/metric_wrappers.py

@@ -150,4 +150,3 @@ def torch_wrapper(y_true, y_pred, sample_weight=None):
             f"Unknown framework: {framework}. "
             f"Supported: 'sklearn', 'xgboost', 'lightgbm', 'pytorch'"
         )
-

src/splinator/monotonic_spline.py

@@ -149,8 +149,20 @@ def _weighted_quantile(values, quantiles, sample_weight=None):
 
     if sample_weight is None:
         return np.quantile(values, quantiles)
-    
+
     sample_weight = np.asarray(sample_weight)
+    if sample_weight.shape != values.shape:
+        raise ValueError("sample_weight must have the same shape as values")
+
+    positive_weight = sample_weight > 0
+    if not np.any(positive_weight):
+        raise ValueError("sample_weight must contain at least one positive value")
+
+    values = values[positive_weight]
+    sample_weight = sample_weight[positive_weight]
+    if np.all(sample_weight == sample_weight[0]):
+        return np.quantile(values, quantiles)
+
     sorted_indices = np.argsort(values)
     sorted_values = values[sorted_indices]
     sorted_weights = sample_weight[sorted_indices]

tests/test_sample_weight.py

@@ -32,7 +32,16 @@ def test_uniform_weights_matches_numpy(self):
         result = _weighted_quantile(X, quantiles, sample_weight=weights)
         expected = np.quantile(X, quantiles)
 
-        np.testing.assert_array_almost_equal(result, expected, decimal=1)
+        np.testing.assert_array_almost_equal(result, expected)
+
+    def test_uniform_weights_preserve_interpolation(self):
+        """Uniform weights should preserve NumPy's interpolated quantiles."""
+        X = np.array([0.0, 10.0])
+        weights = np.ones(2)
+
+        result = _weighted_quantile(X, [0.5], sample_weight=weights)
+
+        np.testing.assert_array_equal(result, np.array([5.0]))
 
     def test_doubled_weights_equivalent_to_duplication(self):
         """Doubling a sample's weight should be like duplicating it."""
@@ -69,6 +78,15 @@ def test_knots_with_weights(self):
 
         assert len(knots) == 4
 
+    def test_uniform_weights_match_unweighted_knots(self):
+        """Uniform weights should not change knot placement."""
+        X = np.array([0.0, 10.0])
+        weights = np.ones(2)
+
+        knots = _fit_knots(X, num_knots=2, sample_weight=weights)
+
+        np.testing.assert_array_equal(knots, _fit_knots(X, num_knots=2))
+
 
 class TestLossGradHessWithWeights:
     """Test LossGradHess class with sample weights."""
@@ -213,6 +231,10 @@ def test_sample_weight_validation(self):
         # Wrong length
         with pytest.raises(ValueError, match="sample_weight has .* samples"):
             model.fit(X, y, sample_weight=np.ones(50))
+
+        # Wrong dimensionality
+        with pytest.raises(ValueError, match="1-D"):
+            model.fit(X, y, sample_weight=np.ones((100, 2)))
 
         # Negative weights
         with pytest.raises(ValueError, match="non-negative"):