fix: r2_score() to use da.where() for broadcasting

Resolves "cannot broadcast shape (nan,) to shape (nan,)" errors.

Author: wietzesuijker

dask_ml/metrics/regression.py

@@ -162,18 +162,13 @@ def r2_score(
     numerator = (weight * (y_true - y_pred) ** 2).sum(axis=0, dtype="f8")
     denominator = (weight * (y_true - y_true.mean(axis=0)) ** 2).sum(axis=0, dtype="f8")
 
-    nonzero_denominator = denominator != 0
-    nonzero_numerator = numerator != 0
-    valid_score = nonzero_denominator & nonzero_numerator
-    output_chunks = getattr(y_true, "chunks", [None, None])[1]
-    output_scores = da.ones([y_true.shape[1]], chunks=output_chunks)
-    with np.errstate(all="ignore"):
-        output_scores[valid_score] = 1 - (
-            numerator[valid_score] / denominator[valid_score]
-        )
-        output_scores[nonzero_numerator & ~nonzero_denominator] = 0.0
-
-    result = output_scores.mean(axis=0)
+    score = da.where(
+        numerator == 0,
+        1.0,
+        da.where(denominator != 0, 1 - numerator / denominator, 0.0),
+    )
+
+    result = score.mean(axis=0)
     if compute:
         result = result.compute()
     return result

tests/metrics/test_regression.py

@@ -116,3 +116,19 @@ def test_regression_metrics_do_not_support_weighted_multioutput(metric_pairs):
 
     with pytest.raises((NotImplementedError, ValueError), match=error_msg):
         _ = m1(a, b, multioutput=weights)
+
+
+def test_r2_score_with_different_chunk_patterns():
+    """Test r2_score with different chunking configurations."""
+    # Create arrays with compatible but different chunk patterns
+    a = da.random.uniform(size=(100,), chunks=25)  # 4 chunks
+    b = da.random.uniform(size=(100,), chunks=20)  # 5 chunks
+    result = dask_ml.metrics.r2_score(a, b)
+    assert isinstance(result, float)
+    # Create arrays with different chunk patterns
+    a_multi = da.random.uniform(size=(100, 3), chunks=(25, 3))  # 4 chunks
+    b_multi = da.random.uniform(size=(100, 3), chunks=(20, 3))  # 5 chunks
+    result_multi = dask_ml.metrics.r2_score(
+        a_multi, b_multi, multioutput="uniform_average"
+    )
+    assert isinstance(result_multi, float)