add MAPE to regression metrics (fixes #691)

dask_ml/metrics/__init__.py

@@ -6,6 +6,7 @@
 )
 from .regression import (  # noqa
     mean_absolute_error,
+    mean_absolute_percentage_error,
     mean_squared_error,
     mean_squared_log_error,
     r2_score,

dask_ml/metrics/regression.py

@@ -81,6 +81,33 @@ def mean_absolute_error(
     return result
 
 
+@derived_from(sklearn.metrics)
+def mean_absolute_percentage_error(
+    y_true: ArrayLike,
+    y_pred: ArrayLike,
+    sample_weight: Optional[ArrayLike] = None,
+    multioutput: Optional[str] = "uniform_average",
+    compute: bool = True,
+) -> ArrayLike:
+    _check_sample_weight(sample_weight)
+    epsilon = np.finfo(np.float64).eps
+    mape = abs(y_pred - y_true) / da.maximum(y_true, epsilon)
+    output_errors = mape.mean(axis=0)
+
+    if isinstance(multioutput, str) or multioutput is None:
+        if multioutput == "raw_values":
+            if compute:
+                return output_errors.compute()
+            else:
+                return output_errors
+    else:
+        raise ValueError("Weighted 'multioutput' not supported.")
+    result = output_errors.mean()
+    if compute:
+        result = result.compute()
+    return result
+
+
 @derived_from(sklearn.metrics)
 def r2_score(
     y_true: ArrayLike,

tests/metrics/test_regression.py

@@ -7,12 +7,20 @@
 import dask_ml.metrics
 
 
-@pytest.fixture(params=["mean_squared_error", "mean_absolute_error", "r2_score"])
+@pytest.fixture(
+    params=[
+        "mean_squared_error",
+        "mean_absolute_error",
+        "mean_absolute_percentage_error",
+        "r2_score",
+    ]
+)
 def metric_pairs(request):
     """Pairs of (dask-ml, sklearn) regression metrics.
 
     * mean_squared_error
     * mean_absolute_error
+    * mean_absolute_percentage_error
     * r2_score
     """
     return (