make regression metrics 'multioutput' behavior consistent with scikit-learn (fixes dask#818) (dask#820)

* make regression metrics 'multioutput' behavior consistent with scikit-learn

* add test on error messages

* linting

Author: jameslamb

dask_ml/metrics/regression.py

@@ -16,7 +16,7 @@ def _check_sample_weight(sample_weight: Optional[ArrayLike]):
 def _check_reg_targets(
     y_true: ArrayLike, y_pred: ArrayLike, multioutput: Optional[str]
 ):
-    if multioutput != "uniform_average":
+    if multioutput is not None and multioutput != "uniform_average":
         raise NotImplementedError("'multioutput' must be 'uniform_average'")
 
     if y_true.ndim == 1:
@@ -40,12 +40,12 @@ def mean_squared_error(
     _check_sample_weight(sample_weight)
     output_errors = ((y_pred - y_true) ** 2).mean(axis=0)
 
-    if isinstance(multioutput, str):
+    if isinstance(multioutput, str) or multioutput is None:
         if multioutput == "raw_values":
-            return output_errors
-        elif multioutput == "uniform_average":
-            # pass None as weights to np.average: uniform mean
-            multioutput = None
+            if compute:
+                return output_errors.compute()
+            else:
+                return output_errors
     else:
         raise ValueError("Weighted 'multioutput' not supported.")
     result = output_errors.mean()
@@ -67,12 +67,12 @@ def mean_absolute_error(
     _check_sample_weight(sample_weight)
     output_errors = abs(y_pred - y_true).mean(axis=0)
 
-    if isinstance(multioutput, str):
+    if isinstance(multioutput, str) or multioutput is None:
         if multioutput == "raw_values":
-            return output_errors
-        elif multioutput == "uniform_average":
-            # pass None as weights to np.average: uniform mean
-            multioutput = None
+            if compute:
+                return output_errors.compute()
+            else:
+                return output_errors
     else:
         raise ValueError("Weighted 'multioutput' not supported.")
     result = output_errors.mean()
@@ -153,7 +153,7 @@ def r2_score(
     compute: bool = True,
 ) -> ArrayLike:
     _check_sample_weight(sample_weight)
-    _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
+    _, y_true, y_pred, _ = _check_reg_targets(y_true, y_pred, multioutput)
     weight = 1.0
 
     numerator = (weight * (y_true - y_pred) ** 2).sum(axis=0, dtype="f8")

tests/metrics/test_regression.py

@@ -1,8 +1,10 @@
 import numbers
 
 import dask.array as da
+import numpy as np
 import pytest
 import sklearn.metrics
+from dask.array.utils import assert_eq
 
 import dask_ml.metrics
 from dask_ml._compat import SK_024
@@ -72,3 +74,53 @@ def test_mean_squared_log_error():
     result = m1(a, b)
     expected = m2(a, b)
     assert abs(result - expected) < 1e-5
+
+
+@pytest.mark.parametrize("multioutput", ["uniform_average", None])
+def test_regression_metrics_unweighted_average_multioutput(metric_pairs, multioutput):
+    m1, m2 = metric_pairs
+
+    a = da.random.uniform(size=(100,), chunks=(25,))
+    b = da.random.uniform(size=(100,), chunks=(25,))
+
+    result = m1(a, b, multioutput=multioutput)
+    expected = m2(a, b, multioutput=multioutput)
+    assert abs(result - expected) < 1e-5
+
+
+@pytest.mark.parametrize("compute", [True, False])
+def test_regression_metrics_raw_values(metric_pairs, compute):
+    m1, m2 = metric_pairs
+
+    if m1.__name__ == "r2_score":
+        pytest.skip("r2_score does not support multioutput='raw_values'")
+
+    a = da.random.uniform(size=(100, 3), chunks=(25, 3))
+    b = da.random.uniform(size=(100, 3), chunks=(25, 3))
+
+    result = m1(a, b, multioutput="raw_values", compute=compute)
+    expected = m2(a, b, multioutput="raw_values")
+
+    if compute:
+        assert isinstance(result, np.ndarray)
+    else:
+        assert isinstance(result, da.Array)
+
+    assert_eq(result, expected)
+    assert result.shape == (3,)
+
+
+def test_regression_metrics_do_not_support_weighted_multioutput(metric_pairs):
+    m1, _ = metric_pairs
+
+    a = da.random.uniform(size=(100, 3), chunks=(25, 3))
+    b = da.random.uniform(size=(100, 3), chunks=(25, 3))
+    weights = da.random.uniform(size=(3,))
+
+    if m1.__name__ == "r2_score":
+        error_msg = "'multioutput' must be 'uniform_average'"
+    else:
+        error_msg = "Weighted 'multioutput' not supported."
+
+    with pytest.raises((NotImplementedError, ValueError), match=error_msg):
+        _ = m1(a, b, multioutput=weights)