ENH Better error message for sparse metrics of neighbors (#12073)

zjpoh · qinhanmin2014 · commit 1049fb1cbcc3 · 2018-09-14T16:23:00.000+08:00
diff --git a/sklearn/neighbors/__init__.py b/sklearn/neighbors/__init__.py
@@ -14,7 +14,7 @@
 from .kde import KernelDensity
 from .approximate import LSHForest
 from .lof import LocalOutlierFactor
-from .base import VALID_METRICS
+from .base import VALID_METRICS, VALID_METRICS_SPARSE
 
 __all__ = ['BallTree',
            'DistanceMetric',
@@ -30,4 +30,5 @@
            'KernelDensity',
            'LSHForest',
            'LocalOutlierFactor',
-           'VALID_METRICS']
+           'VALID_METRICS',
+           'VALID_METRICS_SPARSE']
diff --git a/sklearn/neighbors/base.py b/sklearn/neighbors/base.py
@@ -217,9 +217,12 @@ def _fit(self, X):
                               "using brute force")
             if self.effective_metric_ not in VALID_METRICS_SPARSE['brute'] \
                     and not callable(self.effective_metric_):
-
-                raise ValueError("metric '%s' not valid for sparse input"
-                                 % self.effective_metric_)
+                raise ValueError("Metric '%s' not valid for sparse input. "
+                                 "Use sorted(sklearn.neighbors."
+                                 "VALID_METRICS_SPARSE['brute']) "
+                                 "to get valid options. "
+                                 "Metric can also be a callable function."
+                                 % (self.effective_metric_))
             self._fit_X = X.copy()
             self._tree = None
             self._fit_method = 'brute'
